Frank Neumann

Professor Frank Neumann

Professor

School of Computer Science

Faculty of Engineering, Computer and Mathematical Sciences

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

Frank Neumann received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively. He is a professor and leader of the Optimisation and Logistics Group at the School of Computer Science, The University of Adelaide, Australia. Frank has been the general chair of the ACM GECCO 2016. With Kenneth De Jong he organised ACM FOGA 2013 in Adelaide and together with Carsten Witt he has written the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer. He is an Associate Editor of the journals "Evolutionary Computation" (MIT Press) and "IEEE Transactions on Evolutionary Computation" (IEEE). In his work, he considers artificial intelligence and optimisation methods in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of renewable energy, logistics, and mining.

  • artificial intelligence
  • bio-inspired computing
  • optimization
  • renewable energy
  • supply chain management
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  • Appointments
    Date Position Institution name
    2016 Professor The University of Adelaide
    2015 - 2016 Associate Dean (Research) The University of Adelaide
    2013 - 2015 Associate Professor The University of Adelaide
    2011 - 2012 Senior Lecturer The University of Adelaide
    2008 - 2010 Coordinator of the group "Bio-Inspired Computation” Max Planck Institute for Informatics (MPG)
    2006 - 2008 Postdoctoral Researcher Max Planck Institute for Informatics (MPG)
    2002 - 2006 Research Assistant Kiel University

  • Education
    Date Institution name Country Title
    2006 Kiel University Germany PhD
    2002 Kiel University Germany Diplom
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  • Journals
    Year Citation
    2021 Bossek, J., & Neumann, F. (2021). Evolutionary diversity optimization and the minimum spanning tree problem. GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference, abs/2010.10913, 198-206.
    DOI Scopus1
    2021 Neumann, F., Pourhassan, M., & Witt, C. (2021). Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint. Algorithmica, 83(10), 3209-3237.
    DOI WoS2
    2021 Anh, V. D., & Neumann, F. (2021). Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints. THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 35, 12284-12292.
    2021 Weber, D., & Neumann, F. (2021). A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links.. CoRR, abs/2103.03409.
    2021 Shi, F., Neumann, F., & Wang, J. (2021). Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem.. Algorithmica, 83, 906-939.
    DOI
    2021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems.. CoRR, abs/2104.03440.
    2021 Bossek, J., Neumann, A., & Neumann, F. (2021). Exact Counting and Sampling of Optima for the Knapsack Problem.. CoRR, abs/2106.07412.
    2021 Assenmacher, D., Weber, D., Preuss, M., Calero Valdez, A., Bradshaw, A., Ross, B., . . . Grimme, C. (2021). Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social Science Computer Review, 27 pages.
    DOI Scopus1
    2021 Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2021). Time complexity analysis of randomized search heuristics for the dynamic graph coloring problem. Algorithmica, 83(10), 3148-3179.
    DOI
    2021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic strategies for solving complex interacting stockpile blending problem with chance constraints. GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference, abs/2102.05303, 1079-1087.
    DOI
    2021 Shi, F., Neumann, F., & Wang, J. (2021). Time complexity analysis of evolutionary algorithms for 2-hop (1,2)-minimum spanning tree problem. Theoretical Computer Science, 893, 159-175.
    DOI
    2020 Neumann, A., Alexander, B., & Neumann, F. (2020). Evolutionary Image Transition and Painting Using Random Walks.. CoRR, abs/2003.01517.
    2020 Weber, D., & Neumann, F. (2020). Who's in the gang' Revealing coordinating communities in social media. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, abs/2010.08180, 89-93.
    DOI Scopus2 WoS1
    2020 Roostapour, V., Neumann, A., & Neumann, F. (2020). Evolutionary Multi-Objective Optimization for the Dynamic Knapsack Problem.. CoRR, abs/2004.12574.
    2020 Neumann, A., Bossek, J., & Neumann, F. (2020). Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems.. CoRR, abs/2010.11486.
    2020 Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More effective randomized search heuristics for graph coloring through dynamic optimization. CoRR, abs/2005.13825, 1277-1285.
    DOI
    2020 Doerr, B., & Neumann, F. (2020). A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization.. CoRR, abs/2006.16709.
    2020 Ghasemishabankareh, B., Li, X., Ozlen, M., & Neumann, F. (2020). Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions. Applied Soft Computing Journal, 86, 14 pages.
    DOI
    2020 Neumann, A., Alexander, B., & Neumann, F. (2020). Evolutionary image transition and painting using random walks. Evolutionary Computation, 28(4), 643-675.
    DOI Scopus4
    2020 Chin, T. -J., Cai, Z., & Neumann, F. (2020). Robust fitting in computer vision: Easy or hard?. International Journal of Computer Vision, 128(3), 575-587.
    DOI Scopus3 WoS3
    2020 Shi, F., Neumann, F., & Wang, J. (2020). Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem. Algorithmica, 83(4), 906-939.
    DOI Scopus2 WoS1
    2020 Pourhassan, M., Roostapour, V., & Neumann, F. (2020). Runtime analysis of RLS and (1+1) EA for the dynamic weighted vertex cover problem. Theoretical Computer Science, 832, 20-41.
    DOI Scopus1 WoS1
    2020 Friedrich, T., Kötzing, T., Lagodzinski, J., Neumann, F., & Schirneck, M. (2020). Analysis of the (1 + 1) EA on subclasses of linear functions under uniform and linear constraints. Theoretical Computer Science, 832, 3-19.
    DOI Scopus7 WoS4
    2019 Shi, F., Schirneck, M., Friedrich, T., Kötzing, T., & Neumann, F. (2019). Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints. Algorithmica, 81(2), 1-30.
    DOI Scopus8 WoS6
    2019 Neumann, F., Polyakovskiy, S., Skutella, M., Stougie, L., & Wu, J. (2019). A Fully Polynomial Time Approximation Scheme for Packing While Traveling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11409 LNCS, 59-72.
    DOI Scopus6
    2019 Pourhassan, M., & Neumann, F. (2019). Theoretical analysis of local search and simple evolutionary algorithms for the generalized travelling salesperson problem. Evolutionary Computation, 27(3), 525-558.
    DOI Scopus3
    2019 Kerschke, P., Hoos, H., Neumann, F., & Trautmann, H. (2019). Automated algorithm selection: survey and perspectives. Evolutionary Computation, 27(1), 3-45.
    DOI Scopus91 WoS52 Europe PMC1
    2019 Pourhassan, M., Shi, F., & Neumann, F. (2019). Parameterized analysis of multiobjective evolutionary algorithms and the weighted vertex cover problem. Evolutionary Computation, 27(4), 559-575.
    DOI
    2019 Bossek, J., Kerschke, P., Neumann, A., Neumann, F., & Doerr, C. (2019). One-Shot Decision-Making with and without Surrogates.. CoRR, abs/1912.08956.
    2019 Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives.. Evol. Comput., 27, 3-45.
    DOI
    2019 Shi, F., Schirneck, M., Friedrich, T., Kötzing, T., & Neumann, F. (2019). Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints.. Algorithmica, 81(10), 828-857.
    DOI Scopus1 WoS1
    2019 Bustos, Á. P., Chin, T. -J., Neumann, F., Friedrich, T., & Katzmann, M. (2019). A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints.. CoRR, abs/1902.01534.
    2018 Covantes Osuna, E., Gao, W., Neumann, F., & Sudholt, D. (2018). Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation. Theoretical Computer Science, 832, 123-142.
    DOI Scopus1 WoS1
    2017 Polyakovskiy, S., & Neumann, F. (2017). The Packing While Traveling Problem. European Journal of Operational Research, 258(2), 424-439.
    DOI Scopus8 WoS6
    2017 Doerr, B., Neumann, F., & Sutton, A. (2017). Time Complexity Analysis of Evolutionary Algorithms on Random Satisfiable k-CNF Formulas. Algorithmica, 78(2), 561-586.
    DOI Scopus11 WoS8
    2017 Nallaperuma, S., Neumann, F., & Sudholt, D. (2017). Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem. Evolutionary Computation, 25(4), 673-705.
    DOI Scopus9 WoS4 Europe PMC1
    2017 Bonyadi, M., Michalewicz, Z., Nallaperuma, S., & Neumann, F. (2017). Ahura: a heuristic-based racer for the open racing car simulator. IEEE Transactions on Computational Intelligence and AI in Games, 9(3), 290-304.
    DOI Scopus4 WoS1
    2016 Hoos, H. H., Neumann, F., & Trautmann, H. (2016). Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412).. Dagstuhl Reports, 6, 33-74.
    DOI
    2016 Bonyadi, M. R., Michalewicz, Z., Neumann, F., & Wagner, M. (2016). Evolutionary computation for multicomponent problems: opportunities and future directions.. CoRR, abs/1606.06818.
    2016 Corus, D., Lehre, P. K., Neumann, F., & Pourhassan, M. (2016). A Parameterised Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms.. Evol. Comput., 24, 183-203.
    DOI
    2016 Polyakovskiy, S., Berghammer, R., & Neumann, F. (2016). Solving hard control problems in voting systems via integer programming. European Journal of Operational Research, 250(1), 204-213.
    DOI Scopus1 WoS1
    2016 Corus, D., Lehre, P., Neumann, F., & Pourhassan, M. (2016). A Parameterised Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms. Evolutionary Computation, 24(1), 183-203.
    DOI Scopus10 WoS8 Europe PMC1
    2016 Kaufmann, P., Kramer, O., Neumann, F., & Wagner, M. (2016). Optimization methods in renewable energy systems design. Renewable Energy, 87, 835-836.
    DOI Scopus3 WoS3
    2015 Friedrich, T., & Neumann, F. (2015). Maximizing submodular functions under matroid constraints by evolutionary algorithms. Evolutionary Computation, 23(4), 543-558.
    DOI Scopus30 WoS19
    2015 Nallaperuma, S., Wagner, M., & Neumann, F. (2015). Analyzing the effects of instance features and algorithm parameters for max-min ant system and the traveling salesperson problem. Frontiers in Robotics and AI, 2(JUL), 18-1-18-16.
    DOI Scopus20
    2015 Friedrich, T., Neumann, F., & Thyssen, C. (2015). Multiplicative approximations, optimal hypervolume distributions, and the choice of the reference point. Evolutionary Computation, 23(1), 131-159.
    DOI Scopus7 WoS6 Europe PMC1
    2015 Wagner, M., Neumann, F., & Urli, T. (2015). On the performance of different genetic programming approaches for the SORTING problem. Evolutionary Computation, 23(4), 583-609.
    DOI Scopus6 WoS5
    2015 Nguyen, A., Sutton, A., & Neumann, F. (2015). Population size matters: rigorous runtime results for maximizing the hypervolume indicator. Theoretical Computer Science, 561(Part A), 24-36.
    DOI Scopus6 WoS6
    2015 Wagner, M., Bringmann, K., Friedrich, T., & Neumann, F. (2015). Efficient optimization of many objectives by approximation-guided evolution. European Journal of Operational Research, 243(2), 465-479.
    DOI Scopus22 WoS18
    2015 Polyakovskiy, S., & Neumann, F. (2015). The Packing While Traveling Problem.. CoRR, abs/1512.08831.
    2014 Mei, H., Neumann, F., Yao, X., & Minku, L. L. (2014). Computational Intelligence for Software Engineering (NII Shonan Meeting 2014-13).. NII Shonan Meet. Rep., 2014.
    2014 Kötzing, T., Sutton, A., Neumann, F., & O'Reilly, U. (2014). The Max problem revisited: The importance of mutation in genetic programming. Theoretical Computer Science, 545(C), 94-107.
    DOI Scopus8 WoS6
    2014 Neumann, F., Doerr, B., Lehre, P. K., & Haddow, P. C. (2014). Editorial for the special issue on theoretical foundations of evolutionary computation. IEEE Transactions on Evolutionary Computation, 18(5), 625-627.
    DOI
    2014 Sutton, A., Neumann, F., & Nallaperuma, S. (2014). Parameterized runtime analyses of evolutionary algorithms for the planar Euclidean traveling salesperson problem. Evolutionary Computation, 22(4), 595-628.
    DOI Scopus22 WoS15 Europe PMC1
    2013 Friedrich, T., Kroeger, T., & Neumann, F. (2013). Weighted preferences in evolutionary multi-objective optimization. International Journal of Machine Learning and Cybernetics, 4(2), 139-148.
    DOI Scopus16 WoS15
    2013 Kratsch, S., & Neumann, F. (2013). Fixed-parameter evolutionary algorithms and the vertex cover problem. Algorithmica, 65(4), 754-771.
    DOI Scopus44 WoS32
    2013 Doerr, B., Johannsen, D., Kotzing, T., Neumann, F., & Theile, M. (2013). More effective crossover operators for the all-pairs shortest path problem. Theoretical Computer Science, 471, 12-26.
    DOI Scopus22 WoS17
    2013 Wagner, M., Day, J., & Neumann, F. (2013). A fast and effective local search algorithm for optimizing the placement of wind turbines. Renewable Energy, 51, 64-70.
    DOI Scopus70 WoS67
    2013 Vladislavleva, E., Friedrich, T., Neumann, F., & Wagner, M. (2013). Predicting the energy output of wind farms based on weather data: important variables and their correlation. Renewable Energy, 50, 236-243.
    DOI Scopus59 WoS44
    2013 Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence, 69(2), 151-182.
    DOI Scopus59 WoS34
    2013 Doerr, B., Eremeev, A. V., Neumann, F., Theile, M., & Thyssen, C. (2013). Evolutionary Algorithms and Dynamic Programming. CoRR, abs/1301.4096.
    2012 Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., & Neumann, F. (2012). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. CoRR, abs/1208.2318.
    2012 Berghammer, R., Friedrich, T., & Neumann, F. (2012). Convergence of set-based multi-objective optimization, indicators and deteriorative cycles. Theoretical Computer Science, 456, 2-17.
    DOI Scopus12 WoS9
    2012 Lehre, P. K., Neumann, F., & Rowe, J. E. (2012). Editorial to the special issue on "Theoretical foundations of evolutionary computation". Theoretical Computer Science, 425, 2-3.
    DOI
    2012 Kotzing, T., Neumann, F., Roglin, H., & Witt, C. (2012). Theoretical analysis of two ACO approaches for the traveling salesman problem. Swarm Intelligence, 6(1), 1-21.
    DOI Scopus41 WoS28
    2011 Doerr, B., Eremeev, A., Neumann, F., Theile, M., & Thyssen, C. (2011). Evolutionary algorithms and dynamic programming. Theoretical Computer Science, 412(43), 6020-6035.
    DOI Scopus12 WoS8
    2011 Friedrich, T., Horoba, C., & Neumann, F. (2011). Illustration of fairness in evolutionary multi-objective optimization. Theoretical Computer Science, 412(17), 1546-1556.
    DOI Scopus7 WoS6
    2011 Doerr, B., Neumann, F., Sudholt, D., & Witt, C. (2011). Runtime analysis of the 1-ANT ant colony optimizer. Theoretical Computer Science, 412(17), 1629-1644.
    DOI Scopus21 WoS17
    2011 Neumann, F., Reichel, J., & Skutella, M. (2011). Computing minimum cuts by randomized search heuristics. Algorithmica (New York), 59(3), 323-342.
    DOI Scopus18 WoS18
    2010 Friedrich, T., & Neumann, F. (2010). When to use bit-wise neutrality. Natural Computing, 9(1), 283-294.
    DOI Scopus1 WoS1
    2010 Friedrich, T., He, J., Hebbinghaus, N., Neumann, F., & Witt, C. (2010). Approximating covering problems by randomized search heuristics using multi-objectivemodels. Evolutionary Computation, 18(4), 617-633.
    DOI Scopus71 WoS58 Europe PMC5
    2010 Doerr, B., Neumann, F., & Wegener, I. (2010). Algorithmica (New York): Editorial. Algorithmica (New York), 57(1), 119-120.
    DOI
    2010 Jansen, T., & Neumann, F. (2010). Editorial for the special issue on theoretical aspects of evolutionary multi-objective optimization. Evolutionary Computation, 18(3), 333-334.
    DOI Scopus1 WoS1
    2010 Friedrich, T., Hebbinghaus, N., & Neumann, F. (2010). Plateaus can be harder in multi-objective optimization. Theoretical Computer Science, 411(6), 854-864.
    DOI Scopus6 WoS7
    2010 Neumann, F., & Witt, C. (2010). Ant Colony Optimization and the minimum spanning tree problem. Theoretical Computer Science, 411(25), 2406-2413.
    DOI Scopus48 WoS38
    2009 Doerr, B., & Neumann, F. (2009). In Memoriam: Ingo Wegener. Algorithmica (New York), 58(3), 1-2.
    DOI
    2009 Neumann, F., Sudholt, D., & Witt, C. (2009). Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intelligence, 3(1), 35-68.
    DOI Scopus60
    2009 Neumann, F., & Witt, C. (2009). Runtime analysis of a simple ant colony optimization algorithm. Algorithmica, 54(2), 243-255.
    DOI Scopus65 WoS45
    2009 Friedrich, T., He, J., Hebbinghaus, N., Neumann, F., & Witt, C. (2009). Analyses of simple hybrid algorithms for the vertex cover problem. Evolutionary Computation, 17(1), 3-19.
    DOI Scopus29 WoS23
    2009 Friedrich, T., Hebbinghaus, N., & Neumann, F. (2009). Comparison of simple diversity mechanisms on plateau functions. Theoretical Computer Science, 410(26), 2455-2462.
    DOI Scopus11 WoS8
    2009 Brockhoff, D., Friedrich, T., Hebbinghaus, N., Klein, C., Neumann, F., & Zitzler, E. (2009). On the effects of adding objectives to plateau functions. IEEE Transactions on Evolutionary Computation, 13(3), 591-603.
    DOI Scopus61 WoS50
    2009 Jansen, T., Schmidt, M., Sudholt, D., Witt, C., & Zarges, C. (2009). In Memoriam: Ingo Wegener. EVOLUTIONARY COMPUTATION, 17(1), 1-2.
    DOI
    2008 Neumann, F. (2008). Expected runtimes of evolutionary algorithms for the Eulerian cycle problem. Computers & Operations Research, 35(9), 2750-2759.
    DOI Scopus30 WoS23
    2007 Neumann, F. (2007). Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem. European Journal of Operational Research, 181(3), 1620-1629.
    DOI Scopus58 WoS49
    2007 Neumann, F., & Wegener, I. (2007). Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theoretical Computer Science, 378(1), 32-40.
    DOI Scopus149 WoS115
    2007 Doerr, B., Hebbinghaus, N., & Neumann, F. (2007). Speeding Up Evolutionary Algorithms through Asymmetric Mutation Operators. Evolutionary Computation, 15(4), 401-410.
    DOI Scopus28 WoS24 Europe PMC2
    2006 Neumann, F., & Wegener, I. (2006). Minimum spanning trees made easier via multi-objective optimization. Natural Computing, 5(3), 305-319.
    DOI Scopus92

  • Books
    Year Citation
    2010 Neumann, F., & Witt, C. (2010). Bioinspired computation in combinatorial optimization : algorithms and their computational complexity. Berlin: Springer.
    DOI
    2010 Neumann, F., & Witt, C. (2010). Bioinspired Computation in Combinatorial Optimization. Springer.
    DOI

  • Book Chapters
    Year Citation
    2020 Neumann, F., & Sutton, A. M. (2020). Parameterized complexity analysis of randomized search heuristics. In B. Doerr, & F. Neumann (Eds.), Theory of Evolutionary Computation: Recent Developments in Discrete Optimization (Vol. abs/2001.05120, pp. 213-248). Cham; Switzerland: Springer.
    DOI Scopus3
    2020 Neumann, F., Pourhassan, M., & Roostapour, V. (2020). Analysis of evolutionary algorithms in dynamic and stochastic environments. In B. Doerr, & F. Neumann (Eds.), Theory of Evolutionary Computation: Recent Developments in Discrete Optimization (Vol. abs/1806.08547, pp. 323-358). Cham, Switzerland: Springer.
    DOI Scopus6
    2018 Bonyadi, M., Michalewicz, Z., Wagner, M., & Neumann, F. (2018). Evolutionary computation for multicomponent problems: opportunities and future directions. In S. Datta, & J. P. Davim (Eds.), Optimization in industry: present practices and future scopes (pp. 13-30). Cham, Switzerland: Springer.
    DOI
    2015 Neumann, F., Witt, C., Merz, P., Coello Coello, C., Bartz-Beielstein, T., Schütze, O., . . . Raidl, G. (2015). Part e evolutionary computation. In Springer Handbook of Computational Intelligence (pp. 823-824). Springer Berlin Heidelberg.
    DOI Scopus6
    2012 Schooler, L., Burgess, C., Goldstone, R., Fu, W., Gavrilets, S., Lazer, D., . . . Wiener, J. (2012). Search environments, representation, and encoding. In P. Todd, T. Hills, & T. Robbins (Eds.), Cognitive Search: Evolution, Algorithms, and the Brain (1 ed., pp. 317-333). Cambridge, MA: MIT Press.
    2012 Marshall, J., & Neumann, F. (2012). Foundations of search: a perspective from computer science. In P. Todd, T. Hills, & T. Robbins (Eds.), Cognitive Search: Evolution, Algorithms, and the Brain (1 ed., pp. 257-267). United States: MIT Press.
    2011 Neumann, F., O'Reilly, U., & Wagner, M. (2011). Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions. In R. Riolo, E. Vladislavleva, & J. Moore (Eds.), Genetic Programming Theory and Practice IX (1 ed., pp. 113-128). United States: Springer.
    DOI WoS5
    2011 Friedrich, T., Kroeger, T., & Neumann, F. (2011). Weighted Preferences in Evolutionary Multi-objective Optimization. In AI 2011: Advances in Artificial Intelligence (pp. 291-300). Springer Berlin Heidelberg.
    DOI
    2010 Böttcher, S., Doerr, B., & Neumann, F. (2010). Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem. In Parallel Problem Solving from Nature, PPSN XI (pp. 1-10). Springer Berlin Heidelberg.
    DOI
    2010 Horoba, C., & Neumann, F. (2010). Approximating pareto-optimal sets using diversity strategies in evolutionary multi-objective optimization. In C. Coello Coello, C. Dhaenens, & L. Jourdan (Eds.), Advances in multi-objective nature inspired computing (Vol. 272, pp. 23-44). Berlin: Springer.
    DOI Scopus6
    2009 Neumann, F., Sudholt, D., & Witt, C. (2009). Computational complexity of ant colony optimization and its hybridization with local search. In C. Lim, L. Jain, & S. Dehuri (Eds.), Innovations in Swarm Intelligence: studies in computational intelligence (Vol. 248, pp. 91-120). Berlin: Springer.
    DOI Scopus15
    2008 Neumann, F., & Wegener, I. (2008). Can single-objective optimization profit from multipleobjective optimization?. In J. Knowles, D. Corne, & K. Deb (Eds.), Multi-objective problem solving from nature: from concepts to applications (pp. 115-130). Berlin: Springer.
    DOI
    2006 Kehden, B., & Neumann, F. (2006). A Relation-Algebraic View on Evolutionary Algorithms for Some Graph Problems. In Evolutionary Computation in Combinatorial Optimization (pp. 147-158). Springer Berlin Heidelberg.
    DOI
    2006 Kehden, B., Neumann, F., & Berghammer, R. (2006). Relational Implementation of Simple Parallel Evolutionary Algorithms. In Relational Methods in Computer Science (pp. 161-172). Springer Berlin Heidelberg.
    DOI
    2005 Berghammer, R., & Neumann, F. (2005). RelView – An OBDD-Based Computer Algebra System for Relations. In Computer Algebra in Scientific Computing (pp. 40-51). Springer Berlin Heidelberg.
    DOI
    — Neumann, F., Sudholt, D., & Witt, C. (n.d.). Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search. In Ant Colony Optimization and Swarm Intelligence (pp. 132-143). Springer Berlin Heidelberg.
    DOI

  • Conference Papers
    Year Citation
    2021 Xie, Y., Neumann, A., Neumann, F., & Sutton, A. M. (2021). Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights.. In Proceedings of Genetic and Evolutionary Computation Conference (GECCO'21) Vol. abs/2102.05778 (pp. 1-8). online: ACM.
    DOI
    2021 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2021). Analysis of evolutionary diversity optimisation for permutation problems. In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Vol. abs/2102.11469 (pp. 574-582). ACM.
    DOI Scopus1
    2021 Bossek, J., & Neumann, F. (2021). Evolutionary diversity optimization and the minimum spanning tree problem.. In F. Chicano, & K. Krawiec (Eds.), Proceedings of Genetic and Evolutionary Computation Conference (GECCO21) (pp. 198-206). online: ACM.
    2021 Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms. In F. Chicano, & K. Krawiec (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'21) (pp. 556-564). New York, NY: Association for Computing Machinery.
    DOI
    2021 Neumann, A., Bossek, J., & Neumann, F. (2021). Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions. In F. Chicano, & K. Krawiec (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'21) (pp. 261-269). New York, NY: Association for Computing Machinery.
    DOI Scopus3
    2021 Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Entropy-based evolutionary diversity optimisation for the traveling salesperson problem. In F. Chicano, & K. Krawiec (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'21) (pp. 600-608). New York, NY: Association for Computing Machinery.
    DOI Scopus1
    2021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic strategies for solving complex interacting stockpile blending problem with chance constraints.. In F. Chicano, & K. Krawiec (Eds.), Genetic and Evolutionary Computation Conference (GECCO'21) (pp. 1079-1087). online: ACM.
    2021 Xie, Y., Neumann, A., Neumann, F., & Sutton, A. M. (2021). Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights.. In F. Chicano, & K. Krawiec (Eds.), GECCO (pp. 1187-1194). ACM.
    2021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems. In Proceedings of Genetic and Evolutionary Computation Conference (GECCO'21) (pp. 1-9). online: ACM.
    2021 Neumann, A., Neumann, F., & Qian, C. (2021). Evolutionary submodular optimisation. In K. Krawiec (Ed.), GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 918-940). ACM.
    DOI
    2021 Assimi, H., Neumann, F., Wagner, M., & Li, X. (2021). Novelty particle swarm optimisation for truss optimisation problems. In K. Krawiec (Ed.), GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 67-68). ACM.
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    2021 Friedrich, T., Neumann, F., Rothenberger, R., & Sutton, A. M. (2021). Solving Non-uniform Planted and Filtered Random SAT Formulas Greedily. In C. -M. Li, & F. Manyà (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12831 LNCS (pp. 188-206). Springer International Publishing.
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    2021 Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Computing diverse sets of high quality TSP tours by EAX-based evolutionary diversity optimisation. In S. Finck, M. Hellwig, & P. S. Oliveto (Eds.), Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 9:1). ACM.
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    2021 Reid, W., Neumann, A., Ratcliffe, S., & Neumann, F. (2021). Advanced ore mine optimisation under uncertainty using evolution. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '21) (pp. 1605-1613). New York, NY: Association for Computing Machinery.
    DOI
    2021 Assimi, H., Koch, B., Garcia, C., Wagner, M., & Neumann, F. (2021). Modelling and optimization of run-of-mine stockpile recovery. In Proceedings of the 36th ACM Symposium on Applied Computing (SAC '21) (pp. 450-458). New York, N.Y.: Association for Computing Machinery.
    DOI
    2021 Do, V. A., & Neumann, F. (2021). Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints. In AAAI Vol. 35 (pp. 12284-12292). online: AAAI Press.
    2020 Weber, D., & Neumann, F. (2020). Who's in the Gang? Revealing Coordinating Communities in Social Media.. In M. Atzmüller, M. Coscia, & R. Missaoui (Eds.), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM (pp. 89-93). online: IEEE.
    2020 Sachdeva, R., Neumann, F., & Wagner, M. (2020). The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms.. In H. Yang, K. Pasupa, A. C. -S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020 Vol. 1333 (pp. 220-228). Switzerland: Springer Nature.
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    2020 Neumann, A., & Neumann, F. (2020). Human Interactive EEG-Based Evolutionary Image Animation. In Proceedings of the IEEE 2020 Symposium Series on Computational Intelligence (SSCI) (pp. 678-685). online: IEEE.
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    2020 Hasani Shoreh, M., Hermoza Aragones, R., & Neumann, F. (2020). Using neural networks and diversifying differential evolution for dynamic optimisation. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) (pp. 289-296). online: IEEE.
    DOI
    2020 Assimi, H., Harper, O., Xie, Y., Neumann, A., & Neumann, F. (2020). Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), as published in Frontiers in Artificial Intelligence and Applications Vol. 325 (pp. 307-314). Amsterdam, Netherlands: IOS Press BV.
    DOI Scopus4
    2020 Xie, Y., Neumann, A., & Neumann, F. (2020). Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problem. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20) (pp. 271-279). New York: Association for Computing Machinery.
    DOI Scopus4
    2020 Do, V. A., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving diverse sets of tours for the Travelling Salesperson Problem. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20) (pp. 681-689). New York: Association for Computing Machinery.
    DOI Scopus6
    2020 Doskoc, V., Friedrich, T., Goebel, A., Neumann, A., Neumann, F., & Quinzan, F. (2020). Non-monotone submodular maximization with multiple knapsacks in static and dynamic settings. In IOS Press Vol. 325 (pp. 435-443). Amsterdam, Netherlands: IOS Press.
    DOI
    2020 Bossek, J., Neumann, A., & Neumann, F. (2020). Optimising tours for the weighted traveling salesperson problem and the traveling thief problem: a structural comparison of solutions. In T. Bäck, M. Preuss, A. H. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), as published in Parallel Problem Solving from Nature – PPSN XVI, Part I Vol. 12269 (pp. 346-359). Switzerland: Springer Nature.
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    2020 Do, V., & Neumann, F. (2020). Maximizing submodular or monotone functions under partition matroid constraints by multi-objective evolutionary algorithms. In T. Bäck, M. Preuss, A. H. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), as published in Parallel Problem Solving from Nature – PPSN XVI, Part II Vol. 12270 (pp. 588-603). Switzerland: Springer Nature.
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    2020 Neumann, A., & Neumann, F. (2020). Optimising monotone chance-constrained submodular functions using evolutionary multi-objective algorithms. In T. Bäck, M. Preuss, A. H. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), as published in Parallel Problem Solving from Nature – PPSN XVI, Part I Vol. 12269 (pp. 404-417). Switzerland: Springer Nature.
    DOI Scopus5
    2020 Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks.. In T. Bäck, M. Preuss, A. H. Deutz, H. Wang, C. Doerr, M. T. Emmerich, & H. Trautmann (Eds.), 16th International Conference, PPSN 2020 Vol. 12269 (pp. 111-124). Switzerland: Springer Nature.
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    2020 Doskoc, V., Friedrich, T., Göbel, A., Neumann, A., Neumann, F., & Quinzan, F. (2020). Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings.. In G. D. Giacomo, A. Catalá, B. Dilkina, M. Milano, S. Barro, A. Bugarín, & J. Lang (Eds.), ECAI Vol. 325 (pp. 435-442). IOS Press.
    2020 Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020). Evolving sampling strategies for one-shot optimization tasks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12269 LNCS (pp. 111-124). Springer International Publishing.
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    2020 Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime analysis of evolutionary algorithms with biased mutation for the multi-objective minimum spanning tree problem. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20) Vol. abs/2004.10424 (pp. 551-559). New York: Association for Computing Machinery.
    DOI Scopus1 WoS1
    2020 Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The node weight dependent Traveling Salesperson Problem: Approximation algorithms and randomized search heuristics. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20) Vol. abs/2002.01070 (pp. 1286-1294). New York: Association for Computing Machinery.
    DOI Scopus2
    2020 Hasani Shoreh, M., Hermoza Aragones, R., & Neumann, F. (2020). Neural networks in evolutionary dynamic constrained optimization: computational cost and benefits. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), as published in Frontiers in Artificial Intelligence and Applications Vol. 325 (pp. 275-282). Amsterdam, Netherlands: IOS Press BV.
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    2020 Doerr, B., Doerr, C., Neumann, A., Neumann, F., & Sutton, A. M. (2020). Optimization of chance-constrained submodular functions. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Vol. 34 (pp. 1460-1467). Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
    DOI Scopus6
    2019 Neumann, A., Gao, W., Wagner, M., & Neumann, F. (2019). Evolutionary diversity optimization using multi-objective indicators. In GECCO '19: Proceedings of the 2109 Genetic and Evolutionary Computation Conference Vol. abs/1811.06804 (pp. 1-9). online: Association for Computing Machinery.
    DOI Scopus16 WoS5
    2019 Hasani Shoreh, M., Ameca-Alducin, M., Blaikie, W., Neumann, F., & Schoenauer, M. (2019). On the behaviour of differential evolution for problems with dynamic linear constraints. In Proceedings: 2019 IEEE Congress on Evolutionary Computation (CEC 2019) Vol. abs/1905.04099 (pp. 3045-3052). online: IEEE.
    DOI Scopus3 WoS2
    2019 Neumann, F., & Sutton, A. (2019). Runtime analysis of the (1+1) evolutionary algorithm for the chance-constrained knapsack problem. In FOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 147-153). online: Association for Computing Machinery.
    DOI Scopus4 WoS1
    2019 Shi, F., Neumann, F., & Wang, J. (2019). Runtime analysis of evolutionary algorithms for the depth restricted (1,2)-minimum spanning tree problem. In FOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 133-146). online: Association for Computing Machinery.
    DOI Scopus2
    2019 Hasani Shoreh, M., & Neumann, F. (2019). On the use of diversity mechanisms in dynamic constrained continuous optimization. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), as published in Lecture Notes in Computer Science (Neural Information Processing Proceedings, Part I) Vol. 11953 (pp. 644-657). Cham, Switzerland: Springer.
    DOI Scopus1
    2019 Xie, Y., Harper, O., Assimi, H., Neumann, A., & Neumann, F. (2019). Evolutionary algorithms for the chance-constrained knapsack problem. In GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference Vol. abs/1902.04767 (pp. 338-346). New York: ACM.
    DOI Scopus7 WoS2
    2019 Roostapour, V., Pourhassan, M., & Neumann, F. (2019). Analysis of baseline evolutionary algorithms for the packing while travelling problem. In FOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms Vol. abs/1902.04692 (pp. 124-132). online: Association for Computing Machinery.
    DOI Scopus1 WoS1
    2019 Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving diverse TSP instances by means of novel and creative mutation operators. In FOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 58-71). online: Association for Computing Machinery.
    DOI Scopus13 WoS4
    2019 Bossek, J., Grimme, C., & Neumann, F. (2019). On the benefits of biased edge-exchange mutation for the multi-criteria spanning tree problem. In GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 516-523). New York: ACM.
    DOI Scopus3 WoS2
    2019 Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime analysis of randomized search heuristics for dynamic graph coloring. In GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 1443-1451). New York: ACM.
    DOI Scopus7 WoS3
    2019 Neumann, F., Pourhassan, M., & Witt, C. (2019). Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint. In GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 1506-1514). New York: ACM.
    DOI Scopus5 WoS2
    2019 Doerr, B., Doerr, C., & Neumann, F. (2019). Fast re-optimization via structural diversity. In GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference Vol. abs/1902.00304 (pp. 233-241). New York: ACM.
    DOI Scopus6 WoS3
    2019 Neumann, F., & Sutton, A. M. (2019). Evolving Solutions to Community-Structured Satisfiability Formulas. In THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE (pp. 2346-2353). online: AAAI.
    2019 Friedrich, T., Goebel, A., Neumann, F., Quinzan, F., & Rothenberger, R. (2019). Greedy maximization of functions with bounded curvature under partition matroid constraints. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33 (pp. 2272-2279). Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
    Scopus8 WoS6
    2019 Hasani-Shoreh, M., & Neumann, F. (2019). On the use of diversity mechanisms in dynamic constrained continuous optimization. In Proceedings of 26th International Conference, ICONIP 2019 Vol. abs/1910.06062 (pp. 644-657). Sydney: Springer.
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    2019 Roostapour, V., Neumann, A., Neumann, F., & Friedrich, T. (2019). Pareto optimization for subset selection with dynamic cost constraints. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33 (pp. 2354-2361). Honolulu, Hawaii: Association for the Advancement of Artificial Intelligence.
    DOI Scopus16 WoS8
    2019 Gao, W., Pourhassan, M., Roostapour, V., & Neumann, F. (2019). Runtime analysis of evolutionary multi-objective algorithms optimising the degree and diameter of spanning trees. In Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2019), as published in Lecture Notes in Computer Science Vol. 11411 (pp. 504-515). Cham, Switzerland: Springer.
    DOI Scopus3
    2018 Shi, F., Neumann, F., & Wang, J. (2018). Runtime analysis of randomized search heuristics for the dynamic weighted vertex cover problem. In H. E. Aguirre, & K. Takadama (Eds.), Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO'18) (pp. 1515-1522). New York, NY: Association for Computing Machinery.
    DOI Scopus7 WoS5
    2018 Gao, W., Friedrich, T., Neumann, F., & Hercher, C. (2018). Randomized greedy algorithms for covering problems. In H. E. Aguirre, & K. Takadama (Eds.), Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO'18) (pp. 309-315). New York, NY: Association for Computing Machinery.
    DOI Scopus2 WoS1
    2018 Neumann, A., Gao, W., Doerr, C., Neumann, F., & Wagner, M. (2018). Discrepancy-based evolutionary diversity optimization. In Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO'18) (pp. 991-998). New York, NY: Association for Computing Machinery.
    DOI Scopus18 WoS8
    2018 Neumann, F., & Sutton, A. (2018). Runtime analysis of evolutionary algorithms for the knapsack problem with favorably correlated weights. In A. Auger, C. Fonseca, N. Lourenco, P. Machado, L. Paquete, & D. Whitley (Eds.), Parallel Problem Solving from Nature - PPSN XV: 15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part II Vol. 11102 LNCS (pp. 141-152). Cham: Springer.
    DOI Scopus5 WoS4
    2018 Ghasemishabankareh, B., Ozlen, M., Neumann, F., & Li, X. (2018). A probabilistic tree-based representation for non-convex minimum cost flow problems. In A. Auger, C. Fonseca, N. Lourenco, P. Machado, L. Paquete, & D. Whitley (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11101 LNCS (pp. 69-81). Univ Coimbra, Coimbra, PORTUGAL: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus4 WoS1
    2018 Roostapour, V., Neumann, A., & Neumann, F. (2018). On the performance of baseline evolutionary algorithms on the dynamic knapsack problem. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, & D. Whitley (Eds.), Parallel Problem Solving from Nature - PPSN XV: 15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part I Vol. 11101 LNCS (pp. 158-169). Cham: Springer.
    DOI Scopus16 WoS14
    2018 Neumann, A., & Neumann, F. (2018). On the use of colour-based segmentation in evolutionary image composition. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2018) (pp. 1-8). Piscataway, NJ: IEEE.
    DOI Scopus1
    2018 Neumann, F., Polyakovskiy, S., Skutella, M., Stougie, L., & Wu, J. (2018). A Fully Polynomial Time Approximation Scheme for Packing While Traveling.. In Y. Disser, & V. S. Verykios (Eds.), ALGOCLOUD Vol. 11409 (pp. 59-72). Springer.
    2018 Chin, T. -J., Cai, Z., & Neumann, F. (2018). Robust Fitting in Computer Vision: Easy or Hard?. In CoRR Vol. abs/1802.06464.
    2018 Ameca-Alducin, M., Hasani-Shoreh, M., & Neumann, F. (2018). On the use of repair methods in differential evolution for dynamic constrained optimization. In K. Sim, P. Kaufmann, G. Ascheid, J. Bacardit, S. Cagnoni, C. Cotta, . . . M. Zhang (Eds.), Proceedings of the 21st International Conference on the Applications of Evolutionary Computation (EvoApplications), as published in Lecture Notes in Computer Science Vol. 10784 (pp. 832-847). Cham, Switzerland: Springer International Publishing AG.
    DOI Scopus4 WoS4
    2018 Chin, T. -J., Cai, Z., & Neumann, F. (2018). Robust fitting in computer vision: easy or hard?. In V. Ferrari, M. Herbert, C. Sminchisescu, & Y. Weiss (Eds.), ECCV: European Conference on Computer Vision Vol. abs/1802.06464 (pp. 715-730). Cham, Switzerland: Springer.
    DOI Scopus1 WoS1
    2018 Ameca-Alducin, M., Hasani-Shoreh, M., Blaikie, W., Neumann, F., & Mezura-Montes, E. (2018). A comparison of constraint handling techniques for dynamic constrained optimization problems. In Proceedings: 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 290-297). online: IEEE.
    DOI Scopus6 WoS3
    2018 Pourhassan, M., Roostapour, V., & Neumann, F. (2018). Improved runtime analysis of RLS and (1+1) EA for the dynamic vertex cover problem. In Proceedings of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017) Vol. 2018-January (pp. 1-6). NJ, USA: IEEE.
    DOI Scopus6
    2018 Wu, J., Wagner, M., Polyakovskiy, S., & Neumann, F. (2018). Evolutionary computation plus dynamic programming for the Bi-objective travelling thief problem. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Vol. abs/1802.02434 (pp. 777-784). online: AC.
    DOI Scopus12 WoS1
    2017 Friedrich, T., & Neumann, F. (2017). What's hot in evolutionary computation. In S. P. Singh, & S. Markovitch (Eds.), Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) (pp. 5064-5066). San Francisco, California, USA: AAAI Press.
    Scopus8 WoS3
    2017 Wu, J., Wagner, M., Polyakovskiy, S., & Neumann, F. (2017). Exact approaches for the travelling thief problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10593 LNCS (pp. 110-121). Switzerland: Springer.
    DOI Scopus15
    2017 Neumann, A., Szpak, Z. L., Chojnacki, W., & Neumann, F. (2017). Evolutionary image composition using feature covariance matrices. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017 Vol. abs/1703.03773 (pp. 817-824). online: ACM.
    DOI Scopus11 WoS6
    2017 Neumann, A., Alexander, B., & Neumann, F. (2017). Evolutionary Image Transition Using Random Walks. In Computational Intelligence in Music, Sound, Art and Design - 6th International Conference, EvoMUSART 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings Vol. 10198 LNCS (pp. 230-245). Amsterdam: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus18 WoS9
    2017 Pourhassan, M., Friedrich, T., & Neumann, F. (2017). On the use of the dual formulation for minimum weighted vertex cover in evolutionary algorithms. In FOGA 2017 - Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 37-44). New York, NY, USA: Association for Computing Machinery (ACM).
    DOI Scopus8
    2017 Friedrich, T., Kötzing, T., Lagodzinski, G., Neumann, F., & Schirneck, M. (2017). Analysis of the (1+1) EA on subclasses of linear functions under uniform and linear constraints. In C. Igel, D. Sudholt, & C. Witt (Eds.), Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA 2017) (pp. 45-54). New York: Association for Computing Machinery.
    DOI Scopus6
    2017 Gao, W., Friedrich, T., Kötzing, T., & Neumann, F. (2017). Scaling up local search for minimum vertex cover in large graphs by parallel kernelization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10400 LNAI (pp. 131-143). Melbourne, Australia: Springer Nature.
    DOI Scopus1
    2017 Shi, F., Schirneck, M., Friedrich, T., Kotzing, T., & Neumann, F. (2017). Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints. In GECCO '17: Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 1407-1414). New York, NY: Association for Computing Machinery (ACM).
    DOI Scopus12 WoS9
    2017 Osuna, E., Neumann, F., Gao, W., & Sudholt, D. (2017). Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. In GECCO '17: Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 553-560). Berlin, Germany: Association for Computing Machinery (ACM).
    DOI Scopus7 WoS5
    2017 Neumann, F. (2017). Parameterized analysis of bio-inspired computing. In Proceedings of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017) Vol. 2018-January (pp. 1-3). Piscataway, NJ: IEEE.
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    2017 Wu, J., Wagner, M., Polyakovskiy, S., & Neumann, F. (2017). Exact Approaches for the Travelling Thief Problem. In Y. Shi, K. C. Tan, M. Zhang, K. Tang, X. Li, Q. Zhang, . . . Y. Jin (Eds.), SEAL Vol. 10593 (pp. 110-121). Shenzhen, China: Springer.
    2016 Neumann, F., & Poursoltan, S. (2016). Feature-based algorithm selection for constrained continuous optimisation. In CEC (pp. 1461-1468). Vancouver: IEEE.
    2016 Neumann, A., Alexander, B., & Neumann, F. (2016). The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation. In Neural Information Processing - 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings, Part III Vol. 9949 LNCS (pp. 261-268). Kyoto, JAPAN: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus8 WoS7
    2016 Wu, J., Shekh, S., Sergiienko, N., Cazzolato, B., Ding, B., Neumann, F., & Wagner, M. (2016). Fast and effective optimisation of arrays of submerged wave energy converters. In Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 1045-1052). Denver, CO: Association for Computing Machinery.
    DOI Scopus12 WoS8
    2016 Wu, J., Polyakovskiy, S., & Neumann, F. (2016). On the impact of the renting rate for the unconstrained nonlinear Knapsack problem. In Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 413-419). Denver, CO: ACM.
    DOI Scopus11 WoS7
    2016 Friedrich, T., Kötzing, T., Krejca, M., Nallaperuma, S., Neumann, F., & Schirneck, M. (2016). Fast building block assembly by majority vote crossover. In Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 661-668). Denver, Colorado, USA: Association for Computing Machinery.
    DOI Scopus12 WoS5
    2016 Neumann, F. (2016). Chair's welcome. In GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. iii).
    2016 Chin, T., Kee, Y., Eriksson, A., & Neumann, F. (2016). Guaranteed outlier removal with mixed integer linear programs. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016 (pp. 5858-5866). Las Vegas, NV: IEEE.
    DOI Scopus30 WoS19
    2016 Neumann, F. (2016). Chair's welcome. In GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. iii).
    2016 Doerr, B., Gao, W., & Neumann, F. (2016). Runtime analysis of evolutionary diversity maximization for OneMinMax. In Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 557-564). Denver, Colorado, USA: Association for Computing Machinery.
    DOI Scopus8 WoS5
    2016 Gao, W., Friedrich, T., & Neumann, F. (2016). Fixed-parameter single objective search heuristics for minimum vertex cover. In J. Handl, E. Hart, P. Lewis, M. Lopez-Ibanez, G. Ochoa, & B. Paechter (Eds.), Proceedings of the 14th International Conference on Parallel Problem Solving from Nature Vol. 9921 LNCS (pp. 740-750). Edinburgh, UK: Springer.
    DOI Scopus1
    2016 Pourhassan, M., Shi, F., & Neumann, F. (2016). Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem.. In J. Handl, E. Hart, P. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), PPSN. 14th International conference Parallel Problem Solving from Nature - PPSN XIV Vol. 9921 (pp. 729-739). Online: Springer.
    2015 Neumann, F., & Sutton, A. M. (2015). Parameterized complexity analysis of evolutionary algorithms. In S. Silva, & A. I. Esparcia-Alcázar (Eds.), GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 435-450). ACM.
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    2015 Gao, W., Pourhassan, M., & Neumann, F. (2015). Runtime analysis of evolutionary diversity optimization and the vertex cover problem. In S. Silva, & A. Esparcia-Alcázar (Eds.), Proceedings of the Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 1395-1396). Madrid, Spain: ACM.
    DOI Scopus1
    2015 Neumann, F., & Witt, C. (2015). On the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling. In Q. Yang, & M. Wooldridge (Eds.), Proceedings of the 24th International Joint Conference on Artificial Intelligence Vol. 2015-January (pp. 3742-3748). Buenos Aires, Argentina: AAAI Press.
    Scopus23 WoS16
    2015 Poursoltan, S., & Neumann, F. (2015). A feature-based comparison of evolutionary computing techniques for constrained continuous optimisation. In S. Arik, T. Huang, W. Lai, & Q. Liu (Eds.), Proceedings of the 22nd International Conference on Neural Information Processing Vol. 9491 (pp. 332-343). Istanbul, Turkey: Springer.
    DOI Scopus2 WoS2
    2015 Poursoltan, S., & Neumann, F. (2015). A feature-based analysis on the impact of set of constraints for ε-constrained differential evolution. In S. Arik, T. Huang, W. Lai, & Q. Liu (Eds.), Proceedings of the 22nd International Conference on Neural Information Processing Vol. 9491 (pp. 344-355). Istanbul, Turkey: Springer.
    DOI Scopus2 WoS2
    2015 Doerr, B., Neumann, F., & Sutton, A. (2015). Improved runtime bounds for the (1+1) EA on random 3-CNF formulas based on fitness-distance correlation. In S. Silva (Ed.), Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 1415-1422). Madrid, Spain: Association for Ccomputing Machinery.
    DOI Scopus7 WoS4
    2015 Pourhassan, M., Gao, W., & Neumann, F. (2015). Maintaining 2-approximations for the dynamic vertex cover problem using evolutionary algorithms. In S. Silva (Ed.), Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 903-910). Madrid, Spain: Association for Computing Machinery.
    DOI Scopus20 WoS13
    2015 Pourhassan, M., & Neumann, F. (2015). On the impact of local search operators and variable neighbourhood search for the generalized travelling salesperson problem. In S. Silva (Ed.), Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 465-472). Madrid, Spain: Assocation for Computing Machinery.
    DOI Scopus5 WoS3
    2015 Polyakovskiy, S., & Neumann, F. (2015). Packing while traveling: mixed integer programming for a class of nonlinear knapsack problems. In L. Michel (Ed.), Integration of AI and OR Techniques in Constraint Programming Vol. 9075 (pp. 332-346). Barcelona, Spain: Springer.
    DOI Scopus10 WoS7
    2014 Friedrich, T., & Neumann, F. (2014). Maximizing submodular functions under matroid constraints by multi-objective evolutionary algorithms. In T. Bartz-Beielstein, J. Branke, B. Filipic, & J. Smith (Eds.), (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8672 (pp. 922-931). Ljubljana, Slovenia: Springer.
    DOI Scopus12 WoS7
    2014 Poursoltan, S., & Neumann, F. (2014). A feature-based analysis on the impact of linear constraints for ε-constrained differential evolution. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation (pp. 3088-3095). Beijing, China: Institute of Electrical and Electronics Engineers.
    DOI Scopus3 WoS3
    2014 Wagner, M., & Neumann, F. (2014). Single- and multi-objective genetic programming: new runtime results for sorting. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 125-132). Beijing: IEEE.
    DOI Scopus6 WoS3
    2014 Polyakovskiy, S., Bonyadi, M., Wagner, M., Michalewicz, Z., & Neumann, F. (2014). A comprehensive benchmark set and heuristics for the traveling thief problem. In GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference (pp. 477-484). Vancouver, Canada: Association for Computing Machinery.
    DOI Scopus72 WoS44
    2014 Nallaperuma, S., Neumann, F., Bonyadi, M., & Michalewicz, Z. (2014). EVOR: An online evolutionary algorithm for car racing games. In C. Igel (Ed.), Proceedings of the 2014 Genetic and Evolutionary Computation Conference (pp. 317-324). Vancouver, Canada: Association for Computing Machinery.
    DOI Scopus5 WoS5
    2014 Gao, W., & Neumann, F. (2014). Runtime analysis for maximizing population diversity in single-objective optimization. In C. Igel (Ed.), Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (pp. 777-784). Vancouver, Canada: Association for Computing Machinery.
    DOI Scopus12 WoS5
    2014 Nallaperuma, S., Neumann, F., & Sudholt, D. (2014). A fixed budget analysis of randomized search heuristics for the traveling salesperson problem. In C. Igel (Ed.), Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (pp. 807-814). Vancouver, Canada: Association for Ccomputing Machinery.
    DOI Scopus8 WoS6
    2014 Neumann, F., & Sutton, A. M. (2014). Parameterized complexity analysis of evolutionary algorithms. In D. V. Arnold, & E. Alba (Eds.), GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (pp. 607-621). ACM.
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    2014 Nallaperuma, S., Wagner, M., & Neumann, F. (2014). Parameter prediction based on features of evolved instances for ant colony optimization and the traveling salesperson problem. In T. Bartz-Beielstein, J. Branke, B. Filipič, & J. Smith (Eds.), Proceedings of the 13th International Conference on Parallel Problem Solving from Nature Vol. 8672 (pp. 100-109). Ljubljana, Slovenia: Springer Verlag.
    DOI Scopus9 WoS7
    2014 Nguyen, A., Wagner, M., & Neumann, F. (2014). User preferences for approximation-guided multi-objective evolution. In G. Dick, & et al. (Eds.), Proceedings of the 10th International Conference on Simulated Evolution and Learning Vol. 8886 (pp. 251-262). Online: Springer.
    DOI Scopus3 WoS2
    2014 Neumann, F., & Nguyen, A. (2014). On the impact of utility functions in interactive evolutionary multi-objective optimization. In Proceeding of the 10th International Conference on Simulated Evolution and Learning Vol. 8886 (pp. 419-430). Dunedin, New Zealand: Springer Verlag.
    DOI Scopus1
    2014 Sutton, A., & Neumann, F. (2014). Runtime analysis of evolutionary algorithms on randomly constructed high-density satisfiable 3-CNF formulas. In Proceedings of the 13th International Conference on Parallel Problem Solving from Nature Vol. 8672 (pp. 942-951). Ljubljana, Slovenia: Springer, Cham.
    DOI Scopus9 WoS6
    2013 Neumann, F., & Witt, C. (2013). Bioinspired computation in combinatorial optimization - Algorithms and their computational complexity. In C. Blum, & E. Alba (Eds.), GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (pp. 567-590). ACM.
    DOI Scopus9
    2013 Neumann, F., & Jong, K. D. (2013). Foreword. In FOGA 2013 - Proceedings of the 12th ACM Workshop on Foundations of Genetic Algorithms Vol. 58 (pp. I-II). CSIRO PUBLISHING.
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    2013 Wagner, M., Day, J., Jordan, C., Kroeger, T., & Neumann, F. (2013). Evolving pacing strategies for team pursuit track cycling. In Proceedings of Advances in Metaheuristics Vol. abs/1104.0775 (pp. 61-76). New York: Springer.
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    2013 Nguyen, A., Sutton, A., & Neumann, F. (2013). Population size matters: rigorous runtime results for maximizing the hypervolume indicator. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (pp. 1613-1620). online: ACM.
    DOI Scopus2 WoS2
    2013 Corus, D., Lehre, P., & Neumann, F. (2013). The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO'13 (pp. 519-526). online: ACM.
    DOI Scopus7 WoS5
    2013 Nallaperuma, S., Wagner, M., Neumann, F., Bischi, B., Mersmann, O., & Trautmann, H. (2013). A feature-based comparison of local search and the Christofides algorithm for the travelling salesperson problem. In Proceedings of the 12th Workshop on Foundations of Genetic Algorithms, FOGA XII (pp. 147-159). online: ACM.
    DOI Scopus23
    2013 Tran, R., Wu, J., Denison, C., Ackling, T., Wagner, M., & Neumann, F. (2013). Fast and effective multi-objective optimisation of wind turbine placement. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO'13 (pp. 1381-1388). online: ACM.
    DOI Scopus25 WoS22
    2013 Wagner, M., & Neumann, F. (2013). A fast approximation-guided evolutionary multi-objective algorithm. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (pp. 687-694). online: ACM.
    DOI Scopus54 WoS46
    2013 Nallaperuma, S., Wagner, M., & Neumann, F. (2013). Ant colony optimisation and the travelling salesperson problem - hardness, features and parameter settings. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO'13 (pp. 13-14). Amsterdam, Netherlands: ACM.
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    2013 Nallaperuma, S., Sutton, A., & Neumann, F. (2013). Parameterized complexity analysis and more effective construction methods for ACO algorithms and the Euclidean traveling salesperson problem. In 2013 IEEE Congress on Evolutionary Computation (CEC) (pp. 2045-2052). United States: IEEE.
    DOI Scopus5 WoS4
    2013 Nallaperuma, S., Sutton, A., & Neumann, F. (2013). Fixed-parameter evolutionary algorithms for the euclidean traveling salesperson problem. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (pp. 2037-2044). United States: IEEE.
    DOI Scopus5 WoS2
    2013 Neumann, F., & Jong, K. A. D. (Eds.) (2013). Foundations of Genetic Algorithms XII, FOGA '13, Adelaide, SA, Australia, January 16-20, 2013. In FOGA. ACM.
    2012 Sutton, A. M., & Neumann, F. (2012). A Parameterized Runtime Analysis of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem.. In J. Hoffmann, & B. Selman (Eds.), AAAI. AAAI Press.
    2012 Urli, T., Wagner, M., & Neumann, F. (2012). Experimental supplements to the computational complexity analysis of genetic programming for problems modelling isolated program semantics. In Proceedings of the12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 Vol. 7491 LNCS (pp. 102-112). Germany: Springer-Verlag.
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    2012 Sutton, A., & Neumann, F. (2012). A parameterized runtime analysis of simple evolutionary algorithms for makespan scheduling. In Proceedings of 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 Vol. 7491 LNCS (pp. 52-61). Germany: Springer-Verlag.
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    2012 Wagner, M., & Neumann, F. (2012). Parsimony pressure versus multi-objective optimization for variable length representations. In Proceedings of the12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 Vol. 7491 LNCS (pp. 133-142). Germany: Springer-Verlag.
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    2012 Veeramachaneni, K., Wagner, M., O'Reilly, U., & Neumann, F. (2012). Optimizing energy output and layout costs for large wind farms using particle swarm optimization. In Proceedings of 2012 IEEE Congress on Evolutionary Computation, CEC 2012 (pp. 1-7). USA: IEEE.
    DOI Scopus29 WoS3
    2012 Yuen, J., Gao, S., Wagner, M., & Neumann, F. (2012). An adaptive data structure for evolutionary multi-objective algorithms with unbounded archives. In Proceedings of 2012 IEEE Congress on Evolutionary Computation, CEC 2012 (pp. 1-8). USA: IEEE.
    DOI Scopus2
    2012 Mainberger, M., Hoffmann, S., Weickert, J., Tang, C., Johannsen, D., Neumann, F., & Doerr, B. (2012). Optimising spatial and tonal data for homogeneous diffusion inpainting. In Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011 Vol. 6667 LNCS (pp. 26-37). Germany: Springer-Verlag.
    DOI Scopus30 WoS27
    2012 Sutton, A., Day, J., & Neumann, F. (2012). A parameterized runtime analysis of evolutionary algorithms for MAX-2-SAT. In Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, GECCO'12 (pp. 433-440). online: Association for Computing Machinery.
    DOI Scopus5 WoS3
    2012 Kotzing, T., Sutton, A., Neumann, F., & O'Reilly, U. (2012). The max problem revisited: the importance of mutation in genetic programming. In Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, GECCO'12 (pp. 1333-1340). online: Association for Computing Machinery.
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    2012 Neumann, F. (2012). Computational complexity analysis of multi-objective genetic programming. In Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, GECCO'12 Vol. abs/1203.4881 (pp. 799-806). online: Association for Computing Machinery.
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    2012 Sutton, A., & Neumann, F. (2012). A parameterized runtime analysis of evolutionary algorithms for the Euclidean traveling salesperson problem. In Proceedings of the 26th National Conference on Artificial Intelligence Vol. 2 (pp. 1105-1111). online: AAAI.
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    2012 Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local search and the traveling salesman problem: A feature-based characterization of problem hardness. In Proceedings of the 6th International Conference on Learning and Intelligent Optimization, LION 6 Vol. 7219 LNCS (pp. 115-129). Germany: Springer-Verlag.
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    2011 Friedrich, T., Kroeger, T., & Neumann, F. (2011). Weighted preferences in evolutionary multi-objective optimization. In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence Vol. 7106 LNAI (pp. 291-300). Germany: Springer.
    DOI Scopus18 WoS13
    2011 Kötzing, T., Neumann, F., Sudholt, D., & Wagner, M. (2011). Simple max-min ant systems and the optimization of linear pseudo-boolean functions. In Proceeding: FOGA '11: Proceedings of the 11th Workshop Proceedings on Foundations of Genetic Algorithms Vol. abs/1007.4707 (pp. 209-218). New York: ACM Press.
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    2011 Durrett, G., Neumann, F., & O'Reilly, U. (2011). Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics. In Proceedings of Foundations of Genetic Algorithms XI Vol. abs/1007.4636 (pp. 69-80). New York: ACM Press.
    DOI Scopus29 WoS19
    2011 Wagner, M., Neumann, F., Veeramachaneni, K., & O'Reilly, U. (2011). Optimizing the Layout of 1000 Wind Turbines. In European Wind Energy Association 2011 Scientific Proceedings (pp. 205-209). Brussels, Belgium: UWEA.
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    2011 Bringmann, K., Friedrich, T., Neumann, F., & Wagner, M. (2011). Approximation-guided evolutionary multi-objective optimization. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (pp. 1198-1203). online: IJCAI.
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    2011 Neumann, F., Oliveto, P., Rudolph, G., & Sudholt, D. (2011). On the effectiveness of crossover for migration in parallel evolutionary algorithms. In Proceedings of GECCO'11 (pp. 1587-1594). New York: ACM Press.
    DOI Scopus31 WoS26
    2011 Kotzing, T., Neumann, F., & Spohel, R. (2011). PAC learning and genetic programming. In Proceedings of GECCO'11 (pp. 2091-2096). New York: ACM Press.
    DOI Scopus16 WoS11
    2010 Kotzing, T., Lehre, P., Neumann, F., & Oliveto, P. (2010). Ant colony optimization and the minimum cut problem. In Proceedings of the 12th annual conference on Genetic and evolutionary computation (pp. 1393-1400). New York: ACM Press.
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    2010 Neumann, F., Sudholt, D., & Witt, C. (2010). A few ants are enough: ACO with iteration-best update. In Proceedings of the 12th annual conference on Genetic and evolutionary computation (pp. 63-70). New York: ACM Press.
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    2010 Berghammer, R., Friedrich, T., & Neumann, F. (2010). Set-based multi-objective optimization, indicators, and deteriorative cycles. In Proceedings of the 12th annual conference on Genetic and evolutionary computation (pp. 495-502). New York: ACM Press.
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    2010 Kotzing, T., Neumann, F., Roglin, H., & Witt, C. (2010). Theoretical properties of two ACO approaches for the traveling salesman problem. In M. Dorigo, M. Birattari, G. Di Carlo, R. Doursat, & A. Engelbrecht (Eds.), 7th international conference on Swarm Intelligence Vol. 6234 LNCS (pp. 324-335). Berlin: Springer-Verlag.
    DOI Scopus13 WoS10
    2010 Kratsch, S., Lehre, P., Neumann, F., & Oliveto, P. (2010). Fixed parameter evolutionary algorithms and maximum leaf spanning trees: a matter of mutation. In Proceedings of the 11th international conference on Parallel problem solving from nature Vol. 6238 LNCS (pp. 204-213). Berlin: Springer-Verlag.
    DOI Scopus22 WoS14
    2010 Doerr, B., Johannsen, D., Kotzing, T., Neumann, F., & Theile, M. (2010). More effective crossover operators for the all-pairs shortest path problem. In Proceedings of the 11th international conference on Parallel problem solving from nature Vol. 6238 LNCS (pp. 184-193). Berlin: Springer-Verlag.
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    2010 Neumann, F., & Theile, M. (2010). How crossover speeds up evolutionary algorithms for the multi-criteria all-pairs-shortest-path problem. In R. Schaefer, C. Cotta, J. Kolodziej, & G. Rudolph (Eds.), Proceedings of the 11th international conference on Parallel problem solving from nature Vol. 6238 LNCS (pp. 667-676). Berlin: Springer-Verlag.
    DOI Scopus17 WoS10
    2010 Bottcher, S., Doerr, B., & Neumann, F. (2010). Optimal fixed and adaptive mutation rates for the leadingones problem. In R. Schaefer, C. Cotta, J. Kolodziej, & G. Rudolph (Eds.), Proceedings of the 11th international conference on Parallel problem solving from nature Vol. 6238 LNCS (pp. 1-10). Berlin: Springer-Verlag.
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    2010 Ghandar, A., Michalewicz, Z., & Neumann, F. (2010). Evolving fuzzy rules: evaluation of a new approach. In Proceedings of 8th International Conference SEAL 2010 Vol. 6457 LNCS (pp. 250-259). Germany: Springer.
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    2010 Friedrich, T., & Neumann, F. (2010). Foundations of evolutionary multi-objective optimization. In J. Branke (Ed.), Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication (pp. 2557-2575). Portland, OR: ASSOC COMPUTING MACHINERY.
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    2010 Jansen, T., & Neumann, F. (2010). Computational complexity and evolutionary computation. In J. Branke (Ed.), Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication (pp. 2683-2709). Portland, OR: ASSOC COMPUTING MACHINERY.
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    2009 Helwig, S., Neumann, F., & Wanka, R. (2009). Particle swarm optimization with velocity adaptation. In Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems (pp. 146-151). Washington, DC: IEEE Computer Society.
    DOI Scopus11 WoS7
    2009 Kratsch, S., & Neumann, F. (2009). Fixed-parameter evolutionary algorithms and the vertex cover problem. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (pp. 293-300). New York: ACM Press.
    DOI Scopus12
    2009 Friedrich, T., Horoba, C., & Neumann, F. (2009). Multiplicative approximations and the hypervolume indicator. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (pp. 571-578). New York: ACM Press.
    DOI Scopus47
    2009 Doerr, B., Eremeev, A., Horoba, C., Neumann, F., & Theile, M. (2009). Evolutionary algorithms and dynamic programming. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (pp. 771-777). New York: ACM Press.
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    2009 Neumann, F., Oliveto, P., & Witt, C. (2009). Theoretical analysis of fitness-proportional selection: landscapes and efficiency. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 09) (pp. 835-842). New York: ACM Press.
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    2009 Oliveto, P., Lehre, P., & Neumann, F. (2009). Theoretical Analysis of Rank-based Mutation - Combining Exploration and Exploitation. In 2009 IEEE Congress on Evolutionary Computation (pp. 1455-1462). Piscataway, New Jersey: IEEE.
    DOI Scopus33 WoS24
    2009 Horoba, C., & Neumann, F. (2009). Additive approximations of Pareto-optimal sets by evolutionary multi-objective algorithms. In Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms (pp. 79-86). New York: ACM Press.
    DOI Scopus8 WoS7
    2009 Baswana, S., Biswas, S., Doerr, B., Friedrich, T., Kurur, P., & Neumann, F. (2009). Computing single source shortest paths using single-objective fitness functions. In Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms (pp. 59-66). New York: ACM Press.
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    2009 Jansen, T., & Neumann, F. (2009). Computational complexity and evolutionary computation.. In F. Rothlauf (Ed.), GECCO (Companion) (pp. 3157-3184). ACM.
    2008 Jansen, T., & Neumann, F. (2008). Computational complexity and evolutionary computation. In C. Ryan, & M. Keijzer (Eds.), GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 (pp. 2417-2443). ACM Press.
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    2008 Diedrich, F., Kehden, B., & Neumann, F. (2008). Multi-objective Problems in Terms of Relational Algebra.. In R. Berghammer, B. Möller, & G. Struth (Eds.), RelMiCS Vol. 4988 (pp. 84-98). Springer.
    2008 Neumann, F., Sudholt, D., & Witt, C. (2008). Rigorous analyses for the combination of ant colony optimization and local search. In Ant Colony Optimization and Swarm Intelligence Vol. 5217 LNCS (pp. 132-143). Berlin: Springer.
    DOI Scopus28 WoS22
    2008 Neumann, F., & Reichel, J. (2008). Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms. In Parallel Problem Solving from Nature - PPSN X : LNCS 5199 Vol. 5199 LNCS (pp. 72-81). Berlin: Springer.
    DOI Scopus25 WoS22
    2008 Brockhoff, D., Friedrich, T., & Neumann, F. (2008). Analyzing Hypervolume Indicator Based Algorithms. In Parallel Problem Solving from Nature ¿ PPSN X Vol. 5199 LNCS (pp. 651-660). Berlin: Springer.
    DOI Scopus52 WoS49
    2008 Friedrich, T., Horoba, C., & Neumann, F. (2008). Runtime analyses for using fairness in evolutionary multi-objective optimization. In Parallel Problem Solving from Nature - 10th International Conference Vol. 5199 LNCS (pp. 671-680). Berlin: Springer.
    DOI Scopus1
    2008 Happ, E., Johannsen, D., Klein, C., & Neumann, F. (2008). Rigorous analyses of fitness-proportional selection for optimizing linear functions. In Proceedings of the 10th annual conference on genetic and evolutionary computation (pp. 953-960). New York: ACM New York.
    DOI Scopus49
    2008 Horoba, C., & Neumann, F. (2008). Benefits and drawbacks for the use of ε-dominance in evolutionary multi-objective optimization. In Proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 641-680). New York: ACM New York.
    DOI Scopus31
    2008 Diedrich, F., & Neumann, F. (2008). Using Fast Matrix Multiplication in Bio-Inspired Computation for Complex Optimization Problems. In IEEE World Congress on Computational Intelligence (pp. 3827-3832). New York: IEEE Press.
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    2008 Friedrich, T., & Neumann, F. (2008). When to use bit-wise neutrality. In IEEE World Conference on Computational Intelligence 2008 (pp. 997-1003). Los Alamitos, California: IEEE Press.
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    2008 Diedrich, F., Kehden, B., & Neumann, F. (2008). Multi-objective Problems in Terms of Relational Algebra. In Relations and Kleene Algebra in Computer Science : LNCS 4988 Vol. 4988 LNCS (pp. 84-98). Berlin: Springer.
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    2008 Kroeske, J., Ghandar, A., Michalewicz, Z., & Neumann, F. (2008). Learning fuzzy rules with evolutionary algorithms - An analytic approach. In Proceedings of the 10th International Conference on Parallel Problem Solving from Nature Vol. 5199 LNCS (pp. 1051-1060). Heidelberger Platz 3 Berlin Germany D-14197: Springer-Verlag Berlin.
    DOI Scopus7 WoS5
    2008 Neumann, F., & Witt, C. (2008). Ant colony optimization and the minimum spanning tree problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5313 LNCS (pp. 153-166). Trento, Italy: Springer.
    DOI Scopus19 WoS13
    2008 Neumann, F., Reichel, J., & Skutella, M. (2008). Computing minimum cuts by randomized search heuristics. In Proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 779-786). New York: ACM Press.
    DOI Scopus12
    2007 Jansen, T., & Neumann, F. (2007). Computational complexity and evolutionary computation. In D. Thierens (Ed.), Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, Companion Material (pp. 3225-3250). ACM Press.
    DOI Scopus2
    2007 Friedrich, T., He, J., Hebbinghaus, N., Neumann, F., & Witt, C. (2007). On improving approximate solutions by evolutionary algorithms. In IEEE Congress on Evolutionary Computation 2007 (pp. 2614-2621). Online: IEEE Press.
    DOI Scopus3 WoS2
    2007 Doerr, B., Gnewuch, M., Hebbinghaus, N., & Neumann, F. (2007). A Rigorous View On Neutrality. In 2007 Congress on Evolutionary Computation, CEC 2007 : proceedings, Singapore 25-28th September, 2007. (pp. 2591-2597). Piscataway, N.J.: IEEE.
    DOI Scopus13 WoS10
    2007 Neumann, F., Sudholt, D., & Witt, C. (2007). Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions. In Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics : LNCS 5752 Vol. 4638 LNCS (pp. 61-75). Berlin: Springer.
    DOI Scopus12 WoS11
    2007 Brockhoff, D., Friedrich, T., Hebbinghaus, N., Klein, C., Neumann, F., & Zitzler, E. (2007). Do additional objectives make a problem harder?. In Proceedings of the 9th annual conference on Genetic and evolutionary computation (pp. 765-772). New York: ACM New York.
    DOI Scopus80 WoS43
    2007 Friedrich, T., He, J., Hebbinghaus, N., Neumann, F., & Witt, C. (2007). Approximating covering problems by randomized search heuristics using multi-objective models. In Proceedings of the 9th annual conference on Genetic and evolutionary computation Vol. 14 (pp. 797-804). New York: ACM New York.
    DOI Scopus42 WoS23
    2007 Friedrich, T., Hebbinghaus, N., & Neumann, F. (2007). Rigorous Analyses of Simple Diversity Mechanisms. In Proceedings of the 9th annual conference on Genetic and evolutionary computation (pp. 1219-1225). New York: ACM New York.
    DOI Scopus24 WoS12
    2007 Doerr, B., Neumann, F., Sudholt, D., & Witt, C. (2007). On the Runtime Analysis of the 1-ANT ACO Algorithm. In GECCO '07 Genetic and Evolutionary Computation Conference London, England UK ¿ July 07 - 11, 2007 (pp. 33-40). New York: ACM New York.
    DOI Scopus43 WoS23
    2007 Friedrich, T., Hebbinghaus, N., & Neumann, F. (2007). Plateaus can be harder in multi-objective optimization. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 2622-2629). Singapore, SINGAPORE: IEEE.
    DOI Scopus5 WoS4
    2007 Neumann, F., Sudholt, D., & Witt, C. (2007). Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions.. In T. Stützle, M. Birattari, & H. H. Hoos (Eds.), SLS Vol. 4638 (pp. 61-75). Springer.
    2006 Neumann, F., & Witt, C. (2006). Runtime Analysis of a Simple Ant Colony Optimization Algorithm.. In D. V. Arnold, T. Jansen, M. D. Vose, & J. E. Rowe (Eds.), Theory of Evolutionary Algorithms Vol. 06061. Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
    2006 Neumann, F., & Witt, C. (2006). Runtime Analysis of a Simple Ant Colony Optimization Algorithm.. In T. Asano (Ed.), ISAAC Vol. 4288 (pp. 618-627). Springer.
    2006 Doerr, B., Hebbinghaus, N., & Neumann, F. (2006). Speeding up evolutionary algorithms through restricted mutation operators. In PPSN IX 9th International Conference Vol. 4193 (pp. 978-987). Heidelberger Platz 3 Berlin Germany D-14197: Springer-Verlag Berlin.
    DOI Scopus17 WoS16
    2006 Kehden, B., & Neumann, F. (2006). A Relation-Algebraic View on Evolutionary Algorithms for Some Graph Problems. In Evolutionary Computation in Combinatorial Optimization : 6th European Conference : LNCS 3906 Vol. 3906 (pp. 147-158). Heidelberger Platz 3 Berlin Germany D-14197: Springer-Verlag Berlin.
    DOI Scopus5 WoS5
    2006 Neumann, F., & Laummans, M. (2006). Speeding up approximation algorithms for NP-hard spanning forest problems by multi-objective optimization. In Proceedings of the LATIN 2006: Theoretical Informatics : 7th Latin American Symposium Vol. 3887 LNCS (pp. 745-756). Heidelberger Platz 3 Berlin Germany D-14197: Springer-Verlag Berlin.
    DOI Scopus1 WoS1
    2006 Kehden, B., Neumann, F., & Berghammer, R. (2006). Relational implementation of simple parallel evolutionary algorithms. In Relational Methods in Computer Science : LNCS 3929 Vol. 3929 (pp. 161-172). Heidelberger Platz 3 Berlin Germany D-14197: Springer-Verlag Berlin.
    DOI Scopus1
    2006 Neumann, F., & Witt, C. (2006). Runtime analysis of a simple Ant Colony Optimization algorithm - Extended abstract. In T. Asano (Ed.), ALGORITHMS AND COMPUTATION, PROCEEDINGS Vol. 4288 (pp. 618-+). Calcutta, INDIA: SPRINGER-VERLAG BERLIN.
    WoS27
    2006 Neumann, F., & Witt, C. (2006). Runtime analysis of a simple ant colony optimization algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 4288 LNCS (pp. 618-627). Springer Berlin Heidelberg.
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    2005 Neumann, F., & Wegener, I. (2005). Minimum spanning trees made easier via multi-objective optimization. In H. G. Beyer (Ed.), GECCO 2005 - Genetic and Evolutionary Computation Conference (pp. 763-769). Washington, DC: ASSOC COMPUTING MACHINERY.
    DOI Scopus26 WoS18
    2005 Berghammer, R., & Neumann, F. (2005). RELVIEW - An OBDD-Based Computer Algebra System for Relations. In Computer algebra in scientific computing : 8th International Workshop, CSAC 2005, Kalamata, Greece, September 12-16, 2005 : LNCS 3718 Vol. 3718 LNCS (pp. 40-51). Berlin: Springer.
    DOI Scopus43 WoS47
    2004 Neumann, F., & Wegener, I. (2004). Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. In K. Deb, R. Poli, W. Banzhaf, H. G. Beyer, E. Burke, P. Darwen, . . . A. Tyrrell (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 3102 (pp. 713-724). Seattle, WA: SPRINGER-VERLAG BERLIN.
    DOI Scopus51 WoS43
    2004 Neumann, F. (2004). Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem. In X. Yao, E. Burke, J. A. Lozano, J. Smith, J. J. MereloGuervos, J. A. Bullinaria, . . . H. P. Schwefel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 3242 (pp. 81-90). Univ Birmingham, Sch Comp Sci, Birmingham, ENGLAND: SPRINGER-VERLAG BERLIN.
    DOI Scopus10 WoS11
    2004 Neumann, F. (2004). Expected runtimes of evolutionary algorithms for the eulerian cycle problem. In Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 Vol. 1 (pp. 904-910). Portland, OR: IEEE.
    DOI Scopus25 WoS14
    — Hasani-Shoreh, M., & Neumann, F. (n.d.). On the Use of Diversity Mechanisms in Dynamic Constrained Continuous
    Optimization.

  • Conference Items
    Year Citation
    2020 Neumann, A., & Neumann, F. (2020). Evolutionary computation for digital art. Poster session presented at the meeting of GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. ACM.
    DOI
    2020 Bossek, J., Neumann, A., & Neumann, F. (2020). Evolutionary Diversity Optimisation. Poster session presented at the meeting of Parallel Problem Solving from Nature, PPSN 2020.
    2019 Neumann, A., & Neumann, F. (2019). Evolutionary computation for digital art. Poster session presented at the meeting of Proceedings. The Czech Republic.
    DOI Scopus1 WoS1
    2018 Neumann, A., & Neumann, F. (2018). Evolutionary computation for digital art. Poster session presented at the meeting of Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018. ACM.
    DOI Scopus2
    2016 Urselmann, M., Foussette, C., Janus, T., Tlatlik, S., Gottschalk, A., Emmerich, M. T., . . . Bäck, T. (2016). Selection of a DFO Method for the Efficient Solution of Continuous Constrained Sub-Problems within a Memetic Algorithm for Chemical Process Synthesis.. Poster session presented at the meeting of GECCO. ACM.
    2016 Shim, Y., Auerbach, J. E., & Husbands, P. (2016). Darwinian Dynamics of Embodied Chaotic Exploration.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Urbanowicz, R. J., Browne, W. N., & Kuber, K. (2016). Hands-on Workshop on Learning Classifier Systems.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Pugh, J. K., Soros, L. B., & Stanley, K. O. (2016). An Extended Study of Quality Diversity Algorithms.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Smith, S. L., Cagnoni, S., & Patton, R. (2016). MedGEC'16 Chairs' Welcome.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Tarapore, D., Clune, J., Cully, A., & Mouret, J. -B. (2016). How do Different Encodings Influence the Performance of the MAP-Elites Algorithm?. Poster session presented at the meeting of GECCO. ACM.
    2016 Woodward, J. R., Johnson, C. G., & Brownlee, A. E. (2016). Connecting Automatic Parameter Tuning, Genetic Programming as a Hyper-heuristic, and Genetic Improvement Programming.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Toosi, F. G., Nikolov, N. S., & Eaton, M. (2016). A GA-Inspired Approach to the Reduction of Edge Crossings in Force-Directed Layouts.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Wilson, D., Cussat-Blanc, S., & Luga, H. (2016). The Evolution of Artificial Neurogenesis.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Zuin, G. L., Macedo, Y. P., Chaimowicz, L., & Pappa, G. L. (2016). Discovering Combos in Fighting Games with Evolutionary Algorithms.. Poster session presented at the meeting of GECCO. ACM.
    2016 Zutty, J., Long, D., & Rohling, G. (2016). Increasing the Throughput of Expensive Evaluations Through a Vector Based Genetic Programming Framework.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Alvarez, I. M., Browne, W. N., & Zhang, M. (2016). Human-inspired Scaling in Learning Classifier Systems: Case Study on the n-bit Multiplexer Problem Set.. Poster session presented at the meeting of GECCO. ACM.
    2016 Andersson, M., Bandaru, S., & Ng, A. H. (2016). Tuning of Multiple Parameter Sets in Evolutionary Algorithms.. Poster session presented at the meeting of GECCO. ACM.
    2016 Arrieta, A., Wang, S., Sagardui, G., & Etxeberria, L. (2016). Test Case Prioritization of Configurable Cyber-Physical Systems with Weight-Based Search Algorithms.. Poster session presented at the meeting of GECCO. ACM.
    2016 Arnaldo, I., Hemberg, E., & O'Reilly, U. -M. (2016). Multi-Line Batch Scheduling by Similarity.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Asafuddoula, M., Singh, H. K., & Ray, T. (2016). A CUDA Implementation of an Improved Decomposition Based Evolutionary Algorithm for Multi-Objective Optimization.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Basseur, M., Derbel, B., Goëffon, A., & Liefooghe, A. (2016). Experiments on Greedy and Local Search Heuristics for ddimensional Hypervolume Subset Selection.. Poster session presented at the meeting of GECCO. ACM.
    2016 Brotánková, J., Urli, T., & Kilby, P. (2016). Planning Habitat Restoration with Genetic Algorithms.. Poster session presented at the meeting of GECCO. ACM.
    2016 Bulanova, N., Buzdalova, A., & Buzdalov, M. (2016). Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Chen, Q., Xue, B., Shang, L., & Zhang, M. (2016). Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation.. Poster session presented at the meeting of GECCO. ACM.
    2016 Contreras, S. F., Cortés, C. A., & Guzmán, M. A. (2016). Bio-inspired Multi-objective Optimization Design of a Highly Efficient Squirrel Cage Induction Motor.. Poster session presented at the meeting of GECCO. ACM.
    2016 Cruz, C., Karakiewicz, J., & Kirley, M. (2016). A Morphogenetic Design Strategy Using a Composite CA Model.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Doerr, B., Doerr, C., & Yang, J. (2016). Optimal Parameter Choices via Precise Black-Box Analysis.. Poster session presented at the meeting of GECCO. ACM.
    2016 Fernandes, C. M., Guervós, J. J. M., & Rosa, A. C. (2016). Asynchronous Steady State Particle Swarm.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Fernandez, S., Stützle, T., & Pellicer, P. V. (2016). IAM 2016 Chairs' Welcome & Organization.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Gilan, S. S., Goyal, N., & Dilkina, B. (2016). Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design.. Poster session presented at the meeting of GECCO. ACM.
    2016 Ha, S., Lee, S., & Moon, B. -R. (2016). Inspecting the Latent Space of Stock Market Data with Genetic Programming.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Helmuth, T., McPhee, N. F., & Spector, L. (2016). The Impact of Hyperselection on Lexicase Selection.. Poster session presented at the meeting of GECCO. ACM.
    2016 Helmuth, T., McPhee, N. F., & Spector, L. (2016). Effects of Lexicase and Tournament Selection on Diversity Recovery and Maintenance.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Cramer, S., Kampouridis, M., & Freitas, A. A. (2016). A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives.. Poster session presented at the meeting of GECCO. ACM.
    2016 Huizinga, J., Mouret, J. -B., & Clune, J. (2016). Does Aligning Phenotypic and Genotypic Modularity Improve the Evolution of Neural Networks?. Poster session presented at the meeting of GECCO. ACM.
    2016 Kamizono, M., Shimomura, K., Tajiri, M., & Ono, S. (2016). Two-Dimensional Barcode Decoration Using Module-wise Non-systematic Coding and Cooperative Evolution by User and System.. Poster session presented at the meeting of GECCO. ACM.
    2016 Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models.. Poster session presented at the meeting of GECCO. ACM.
    2016 Ge, Y. -F., Yu, W. -J., & Zhang, J. (2016). Diversity-Based Multi-Population Differential Evolution for Large-Scale Optimization.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Greensmith, J., Jackson, A. M., & Spendlove, I. (2016). Exploiting the Plasticity of Primary and Secondary Response Mechanisms in Artificial Immune Systems.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Martínez, S. Z., Moraglio, A., Aguirre, H. E., & Tanaka, K. (2016). Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition.. Poster session presented at the meeting of GECCO. ACM.
    2016 Salomon, S., Purshouse, R. C., Giaghiozis, I., & Fleming, P. J. (2016). A Toolkit for Generating Scalable Stochastic Multiobjective Test Problems.. Poster session presented at the meeting of GECCO. ACM.
    2016 Abdolmaleki, A., Lau, N., Reis, L. P., & Neumann, G. (2016). Contextual Stochastic Search.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Doncieux, S., Auerbach, J. E., Duro, R. J., & Vladar, H. P. D. (2016). Evolution in Cognition 2016 Chairs' Welcome.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Espinoza-Nevárez, D., Ortiz-Bayliss, J. C., Terashima-Marín, H., & Gatica, G. (2016). Selection and Generation Hyper-heuristics for Solving the Vehicle Routing Problem with Time Windows.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Gómez, R. H., Coello, C. A. C., & Torres, E. A. (2016). A Multi-Objective Evolutionary Algorithm based on Parallel Coordinates.. Poster session presented at the meeting of GECCO. ACM.
    2016 Kenny, A., Li, X., Qin, A. K., & Ernst, A. T. (2016). A Population-based Local Search Technique with Random Descent and Jump for the Steiner Tree Problem in Graphs.. Poster session presented at the meeting of GECCO. ACM.
    2016 Mihoc, T. D., Lung, R. I., Gaskó, N., & Suciu, M. A. (2016). Approximation of (k, t)-robust Equilibria.. Poster session presented at the meeting of GECCO. ACM.
    2016 Roy, P. C., Islam, M. M., & Deb, K. (2016). Best Order Sort: A New Algorithm to Non-dominated Sorting for Evolutionary Multi-objective Optimization.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Santos, V. L. A., Arroyo, J. E. C., & Carvalho, T. F. (2016). Iterated Local Search Based Heuristic for Scheduling Jobs on Unrelated Parallel Machines with Machine Deterioration Effect.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Steenkiste, S. V., Koutník, J., Driessens, K., & Schmidhuber, J. (2016). A Wavelet-based Encoding for Neuroevolution.. Poster session presented at the meeting of GECCO. ACM.
    2016 Bartoli, A., Lorenzo, A. D., Medvet, E., & Tarlao, F. (2016). On the Automatic Construction of Regular Expressions from Examples (GP vs. Humans 1-0).. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Bergé, P., Guiban, K. L., Rimmel, A., & Tomasik, J. (2016). Search Space Exploration and an Optimization Criterion for Hard Design Problems.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Duarte, M., Gomes, J. C., Oliveira, S. M., & Christensen, A. L. (2016). EvoRBC: Evolutionary Repertoire-based Control for Robots with Arbitrary Locomotion Complexity.. Poster session presented at the meeting of GECCO. ACM.
    2016 Ellefsen, K. O., Lepikson, H. A., & Albiez, J. C. (2016). Planning Inspection Paths through Evolutionary Multi-objective Optimization.. Poster session presented at the meeting of GECCO. ACM.
    2016 Benítez, C. M. V., Parpinelli, R. S., & Lopes, H. S. (2016). An Ecologically-inspired Parallel Approach Applied to the Protein Structure Reconstruction from Contact Maps.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Garbelini, J. C., Kashiwabara, A. Y., & Sanches, D. S. (2016). Discovery Motifs by Evolutionary Computation.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Labidi, M. K., Diarrassouba, I., Mahjoub, A. R., & Omrane, A. (2016). A Parallel Hybrid Genetic Algorithm for the k-Edge-Connected Hop-Constrained Network Design Problem.. Poster session presented at the meeting of GECCO. ACM.
    2016 Mariani, T., Guizzo, G., Vergilio, S. R., & Pozo, A. T. (2016). Grammatical Evolution for the Multi-Objective Integration and Test Order Problem.. Poster session presented at the meeting of GECCO. ACM.
    2016 Martí, L., Tchango, A. F., Navarro, L., & Schoenauer, M. (2016). VorAIS: A Multi-Objective Voronoi Diagram-based Artificial Immune System.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Medernach, D., Fitzgerald, J., Azad, R. M. A., & Ryan, C. (2016). A New Wave: A Dynamic Approach to Genetic Programming.. Poster session presented at the meeting of GECCO. ACM.
    2016 Nielsen, S. S., Torres, C. F., Danoy, G., & Bouvry, P. (2016). Tackling the IFP Problem with the Preference-Based Genetic Algorithm.. Poster session presented at the meeting of GECCO. ACM.
    2016 Pagán, J., Risco-Martín, J. L., Moya, J. M., & Ayala, J. L. (2016). Grammatical Evolutionary Techniques for Prompt Migraine Prediction.. Poster session presented at the meeting of GECCO. ACM.
    2016 Perez-Carabaza, S., Besada-Portas, E., Orozco, J. A. L., & Cruz, J. M. D. L. (2016). A Real World Multi-UAV Evolutionary Planner for Minimum Time Target Detection.. Poster session presented at the meeting of GECCO. ACM.
    2016 Rocha, G. K., Custódio, F. L., Barbosa, H. J., & Dardenne, L. E. (2016). Using Crowding-Distance in a Multiobjective Genetic Algorithm for Protein Structure Prediction.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Smith, R. J., Zincir-Heywood, A. N., Heywood, M. I., & Jacobs, J. T. (2016). Initiating a Moving Target Network Defense with a Real-time Neuro-evolutionary Detector.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Sosa-Ascencio, A., Terashima-Marín, H., Ortiz-Bayliss, J. C., & Conant-Pablos, S. E. (2016). Grammar-based Selection Hyper-heuristics for Solving Irregular Bin Packing Problems.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Sotto, L. F. D. P., Coelho, R. C., & Melo, V. V. D. (2016). Classification of Cardiac Arrhythmia by Random Forests with Features Constructed by Kaizen Programming with Linear Genetic Programming.. Poster session presented at the meeting of GECCO. ACM.
    2016 Suciu, M. A., Lung, R. I., & Gaskó, N. (2016). Game theory, Extremal optimization, and Community Structure Detection in Complex Networks.. Poster session presented at the meeting of GECCO. ACM.
    2016 Tran, C. T., Zhang, M., Andreae, P., & Xue, B. (2016). Directly Constructing Multiple Features for Classification with Missing Data using Genetic Programming with Interval Functions.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Khadka, S., Tumer, K., Colby, M. K., Tucker, D., Pezzini, P., & Bryden, K. M. (2016). Neuroevolution of a Hybrid Power Plant Simulator.. Poster session presented at the meeting of GECCO. ACM.
    2016 Abdolmaleki, A., Lioutikov, R., Lau, N., Reis, L. P., Peters, J., & Neumann, G. (2016). Model-Based Relative Entropy Stochastic Search.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Bevilacqua, V., Brunetti, A., Triggiani, M., Magaletti, D., Telegrafo, M., & Moschetta, M. (2016). An Optimized Feed-forward Artificial Neural Network Topology to Support Radiologists in Breast Lesions Classification.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Tatsumi, T., Komine, T., Nakata, M., Sato, H., Kovacs, T., & Takadama, K. (2016). Variance-based Learning Classifier System without Convergence of Reward Estimation.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Larsen, S. J., Alkærsig, F. G., Ditzel, H. J., Jurisica, I., Alcaraz, N., & Baumbach, J. (2016). A Simulated Annealing Algorithm for Maximum Common Edge Subgraph Detection in Biological Networks.. Poster session presented at the meeting of GECCO. ACM.
    2016 Liu, X., Li, F., Ding, Y., Wang, L., & Hao, K. (2016). Mechanical Modeling with Particle Swarm Optimization Algorithm for Braided Bicomponent Ureteral Stent.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Lynch, D., Fenton, M., Kucera, S., Claussen, H., & O'Neill, M. (2016). Evolutionary Learning of Scheduling Heuristics for Heterogeneous Wireless Communications Networks.. Poster session presented at the meeting of GECCO. ACM.
    2016 Guervós, J. J. M., Castillo, P. A., García-Sánchez, P., Cuevas, P. D. L., & Valdez, M. G. (2016). NodIO: A Framework and Architecture for Pool-based Evolutionary Computation.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Osaba, E., López-García, P., Masegosa, A. D., Onieva, E., Landaluce, H., & Perallos, A. (2016). TIMON Project: Description and Preliminary Tests for Traffic Prediction Using Evolutionary Techniques.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Kordmahalleh, M. M., Sefidmazgi, M. G., & Homaifar, A. (2016). A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes.. Poster session presented at the meeting of GECCO. ACM.
    2016 Sorrosal, G., Borges, C. E., Holeña, M., Macarulla, A. M., Andonegui, C. M., & Alonso-Vicario, A. (2016). Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Ahrari, A., Lei, H., Sharif, M. A., Deb, K., & Tan, X. (2016). Optimum Design of Artificial Lateral Line Systems for Object Tracking under Uncertain Conditions.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Scheibenpflug, A., Karder, J., Schaller, S., Wagner, S., & Affenzeller, M. (2016). Evolutionary Procedural 2D Map Generation using Novelty Search.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Szubert, M., Kodali, A., Ganguly, S., Das, K., & Bongard, J. C. (2016). Reducing Antagonism between Behavioral Diversity and Fitness in Semantic Genetic Programming.. Poster session presented at the meeting of GECCO. ACM.
    2016 Vallejo, M., Cosgrove, J., Alty, J. E., Smith, S. L., Corne, D. W., & Lones, M. A. (2016). Using Multiobjective Evolutionary Algorithms to Understand Parkinson's Disease.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Auger, A., Brockhoff, D., Hansen, N., Tusar, D., Tusar, T., & Wagner, T. (2016). GECCO'16 Black-Box Optimization Benchmarking Workshop (BBOB-2016): Workshop Chairs' Welcome Message.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Sanches, D. S., Jr, J. B. A. L., & Delbem, A. C. (2016). Multiobjective Discrete Differential Evolution for Service Restoration in Energy Distribution Systems.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Badr, G., Hosny, M., Bintayyash, N., Albilali, E., & Marie-Sainte, S. L. (2016). BeamGA Median: A Hybrid Heuristic Search Framework.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Butcher, S., Strasser, S., Hoole, J., Demeo, B., & Sheppard, J. W. (2016). Relaxing Consensus in Distributed Factored Evolutionary Algorithms.. Poster session presented at the meeting of GECCO. ACM.
    2016 Cumbo, K. C., Heck, S., Tanimoto, I., DeVault, T., Heckendorn, R. B., & Soule, T. (2016). Bee-Inspired Landmark Recognition in Robotic Navigation.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Derbel, B., Liefooghe, A., Zhang, Q., Aguirre, H. E., & Tanaka, K. (2016). Local Search Move Strategies within MOEA/D.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Fernandez, S., Valledor, P., Díaz, D., Malatsetxebarria, E., & Iglesias, M. (2016). Criticality of Response Time in the usage of Metaheuristics in Industry.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Salgado, R., Prieto, A., Bellas, F., Calvo-Varela, L., & Duro, R. J. (2016). Neuroevolutionary Motivational Engine for Autonomous Robots.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Stolfi, D. H., Armas, R., Alba, E., Aguirre, H. E., & Tanaka, K. (2016). Fine Tuning of Traffic in our Cities with Smart Panels: The Quito City Case Study.. Poster session presented at the meeting of GECCO. ACM.
    2016 Vallejo, M., Cosgrove, J., Alty, J. E., Jamieson, S., Smith, S. L., Corne, D. W., & Lones, M. A. (2016). A Multi-Objective Approach to Predicting Motor and Cognitive Deficit in Parkinson's Disease Patients.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Vladar, H. P. D., Fedor, A., Szilágyi, A., Zachar, I., & Szathmáry, E. (2016). An Attractor Network-Based Model with Darwinian Dynamics.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Wang, Y., Qian, Y., Li, Y., Gong, M., & Banzhaf, W. (2016). Artificial Multi-Bee-Colony Algorithm for k-Nearest-Neighbor Fields Search.. Poster session presented at the meeting of GECCO. ACM.
    2016 Colby, M. K., Yliniemi, L. M., Pezzini, P., Tucker, D., Bryden, K. M., & Tumer, K. (2016). Multiobjective Neuroevolutionary Control for a Fuel Cell Turbine Hybrid Energy System.. Poster session presented at the meeting of GECCO. ACM.
    2016 Paula, L. C. D., Soares, A. D. S., Lima, T. W. D., & Coelho, C. J. (2016). Feature Selection using Genetic Algorithm: An Analysis of the Bias-Property for One-Point Crossover.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Khalifa, A., Liebana, D. P., Lucas, S. M., & Togelius, J. (2016). General Video Game Level Generation.. Poster session presented at the meeting of GECCO. ACM.
    2016 Stefano, C. D., Fontanella, F., & Freca, A. S. D. (2016). A Novel GA-based Feature Selection Approach for High Dimensional Data.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Liu, X. F., Zhan, Z. -H., Lin, J. -H., & Zhang, J. (2016). Parallel Differential Evolution Based on Distributed Cloud Computing Resources for Power Electronic Circuit Optimization.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Duro, R. J., Becerra, J. A., Monroy, J., & Caamaño, P. (2016). Considering Memory Networks in the LTM Structure of the Multilevel Darwinist Brain.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Volz, V., Rudolph, G., & Naujoks, B. (2016). Demonstrating the Feasibility of Automatic Game Balancing.. Poster session presented at the meeting of GECCO. ACM.
    2016 Plachkov, A., Abielmona, R. S., Harb, M., Falcon, R., Inkpen, D., Groza, V., & Petriu, E. M. (2016). Automatic Course of Action Generation using Soft Data for Maritime Domain Awareness.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Marmion, M. -E., Aguirre, H. E., Dhaenens, C., Jourdan, L., & Tanaka, K. (2016). Multi-objective Neutral Neighbors': What could be the definition(s)?. Poster session presented at the meeting of GECCO. ACM.
    2016 Langdon, W. B., Vilella, A., Lam, B. Y. H., Petke, J., & Harman, M. (2016). Benchmarking Genetically Improved BarraCUDA on Epigenetic Methylation NGS datasets and nVidia GPUs.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Auger, A., Brockhoff, D., Hansen, N., Tusar, D., Tusar, T., & Wagner, T. (2016). The Impact of Search Volume on the Performance of RANDOMSEARCH on the Bi-objective BBOB-2016 Test Suite.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Auger, A., Brockhoff, D., Hansen, N., Tusar, D., Tusar, T., & Wagner, T. (2016). Benchmarking MATLAB's gamultiobj (NSGA-II) on the Bi-objective BBOB-2016 Test Suite.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Spector, L., McPhee, N. F., Helmuth, T., Casale, M. M., & Oks, J. (2016). Evolution Evolves with Autoconstruction.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Rahat, A. A. -A., Everson, R. M., Fieldsend, J. E., Jin, Y., & Wang, H. (2016). Surrogate-Assisted Evolutionary Optimisation (SAEOpt'16) Chairs' Welcome & Organization.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Panichella, A., Alexandru, C. V., Panichella, S., Bacchelli, A., & Gall, H. C. (2016). A Search-based Training Algorithm for Cost-aware Defect Prediction.. Poster session presented at the meeting of GECCO. ACM.
    2016 McPhee, N. F., Casale, M. M., Finzel, M., Helmuth, T., & Spector, L. (2016). Visualizing Genetic Programming Ancestries.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Auger, A., Brockhoff, D., Hansen, N., Tusar, D., Tusar, T., & Wagner, T. (2016). Benchmarking RM-MEDA on the Bi-objective BBOB-2016 Test Suite.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Auger, A., Brockhoff, D., Hansen, N., Tusar, D., Tusar, T., & Wagner, T. (2016). The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Leclerc, G., Auerbach, J. E., Iacca, G., & Floreano, D. (2016). The Seamless Peer and Cloud Evolution Framework.. Poster session presented at the meeting of GECCO. ACM.
    2016 Auger, A., Brockhoff, D., Hansen, N., Tusar, D., Tusar, T., & Wagner, T. (2016). Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Scott, E. O., & De Jong, K. A. (2016). Evaluation-time bias in quasi-generational and steady-state asynchronous evolutionary algorithms. Poster session presented at the meeting of GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Denver, CO: ASSOC COMPUTING MACHINERY.
    DOI Scopus7 WoS5
    2016 Friedrich, T., Kötzing, T., Krejca, M. S., & Sutton, A. M. (2016). The Benefit of Recombination in Noisy Evolutionary Search.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Friedrich, T., Kötzing, T., Quinzan, F., & Sutton, A. M. (2016). Ant Colony Optimization Beats Resampling on Noisy Functions.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Colmenar, J. M., Winkler, S. M., Kronberger, G., Maqueda, E., Botella, M., & Hidalgo, J. I. (2016). Predicting Glycemia in Diabetic Patients By Evolutionary Computation and Continuous Glucose Monitoring.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Duarte, M., Costa, V., Gomes, J. C., Rodrigues, T., Silva, F., Oliveira, S. M., & Christensen, A. L. (2016). Unleashing the Potential of Evolutionary Swarm Robotics in the Real World.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Kheng, C. W., Ku, D. C., Ng, H. F., Khattab, M., & Chong, S. Y. (2016). Curvature Flight Path for Particle Swarm Optimisation.. Poster session presented at the meeting of GECCO. ACM.
    2016 Martins, M. S., Delgado, M. R. D. B. D. S., Santana, R., Lüders, R., Gonçalves, R. A., & Almeida, C. P. D. (2016). HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm.. Poster session presented at the meeting of GECCO. ACM.
    2016 Guervós, J. J. M., Castillo, P. A., García-Sánchez, P., Cuevas, P. D. L., Rico, N., & Valdez, M. G. (2016). Performance for the Masses: Experiments with A Web Based Architecture to Harness Volunteer Resources for Low Cost Distributed Evolutionary Computation.. Poster session presented at the meeting of GECCO. ACM.
    2016 Paula, L. C. D., Soares, A. D. S., Lima, T. W. D., Filho, A. R., & Coelho, C. J. (2016). Variable Selection for Multivariate Calibration in Chemometrics: A Real-World Application with Building Blocks Disruption Problem.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2016 Dang, D. -C., Friedrich, T., Kötzing, T., Krejca, M. S., Lehre, P. K., Oliveto, P. S., . . . Sutton, A. M. (2016). Escaping Local Optima with Diversity Mechanisms and Crossover.. Poster session presented at the meeting of GECCO. ACM.
    2016 Fernando, C., Banarse, D., Reynolds, M., Besse, F., Pfau, D., Jaderberg, M., . . . Wierstra, D. (2016). Convolution by Evolution: Differentiable Pattern Producing Networks.. Poster session presented at the meeting of GECCO. ACM.
    2016 Larson, A., Bernatskiy, A., Cappelle, C., Livingston, K. R., Livingston, N., Jr, J. H. L., . . . Bongard, J. C. (2016). Recombination Hotspots Promote the Evolvability of Modular Systems.. Poster session presented at the meeting of GECCO (Companion). ACM.
    2012 Neumann, F., & Witt, C. (2012). Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. Poster session presented at the meeting of GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation. Philadelphia, U.S.A.: ACM.
    DOI Scopus13 WoS8
    2011 Friedrich, T., & Neumann, F. (2011). Foundations of evolutionary multi-objective optimization. Poster session presented at the meeting of Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication. Dublin, Ireland: ACM.
    DOI
    2011 Jansen, T., & Neumann, F. (2011). Computational complexity and evolutionary computation. Poster session presented at the meeting of Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication. Dublin, Ireland: ACM.
    DOI

  • Working Paper
    Year Citation
    2018 Neumann, A., Gao, W., Wagner, M., & Neumann, F. (2018). Evolutionary Diversity Optimization Using Multi-Objective Indicators..
    2016 Neumann, A., Alexander, B., & Neumann, F. (2016). The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation.
    2016 Pourhassan, M., Shi, F., & Neumann, F. (2016). Parameterized analysis of multi-objective evolutionary algorithms and the weighted vertex cover problem. SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus11 WoS5
    2016 Neumann, F., & Poursoltan, S. (2016). Feature-based algorithm selection for constrained continuous optimisation. IEEE.
    DOI Scopus3 WoS3
    2015 Hoeltgen, L., Mainberger, M., Hoffmann, S., Weickert, J., Tang, C. H., Setzer, S., . . . Doerr, B. (2015). Optimising Spatial and Tonal Data for PDE-based Inpainting..
    2014 Polyakovskiy, S., Berghammer, R., & Neumann, F. (2014). Solving Hard Control Problems in Voting Systems via Integer Programming..
    2013 Friedrich, T., Neumann, F., & Thyssen, C. (2013). Multiplicative Approximations, Optimal Hypervolume Distributions, and the Choice of the Reference Point..
    2012 Doerr, B., Johannsen, D., Kötzing, T., Neumann, F., & Theile, M. (2012). More Effective Crossover Operators for the All-Pairs Shortest Path Problem.
    2011 Wagner, M., & Neumann, F. (2011). Computational Complexity Results for Genetic Programming and the Sorting Problem.

  • Internet Publications
    Year Citation
    2022 Roostapour, V., Neumann, A., Neumann, F., & Friedrich, T. (2022). Pareto optimization for subset selection with dynamic cost constraints. ELSEVIER.
    DOI
    2020 Doerr, B., Doerr, C., Neumann, A., Neumann, F., & Sutton, A. M. (2020). Optimization of Chance-Constrained Submodular Functions.. AAAI Press.
    2019 Neumann, A., Neumann, F., & Friedrich, T. (2019). Quasi-random Image Transition and Animation..
    2019 Xie, Y., Harper, O., Assimi, H., Neumann, A., & Neumann, F. (2019). Evolutionary algorithms for the chance-constrained knapsack problem..
    2018 Neumann, A., Gao, W., Doerr, C., Neumann, F., & Wagner, M. (2018). Discrepancy-based Evolutionary Diversity Optimization.
    2017 Neumann, A., Neumann, F., & Friedrich, T. (2017). Quasi-random Agents for Image Transition and Animation..
    2016 Neumann, A., Alexander, B., & Neumann, F. (2016). Evolutionary Image Transition Based on Theoretical Insights of Random Processes..
  • NHMRC Ideas Grant "Optimising time use for health and wellbeing", National Health and Medical Research Council, 2020-2023 (led by Dorothea Dumuid at the University of South Australia, several investigators).
  • ARC Discovery Project "Evolutionary diversity optimisation", Australian Research Council, 2019-2021 (with Tobias Friedrich).
  • Humboldt Fellowship for Experienced Researchers, Alexander von Humboldt Foundation, 2019-2021.
  • ARC Industrial Transformation Training Centre for Integrated Operations for Complex Resources, Australian Research Council, 2019-2023 (several chief investigators and industry partners).
  • Project "Detection and classification of malicious virtual grassroots influence campaigns in social media", Australia-Germany Joint Research Co-operation Scheme, 2020-2021 (with Lewis Mitchell, Christian Grimme, Mewish Nasim, Derek Weber, Dennis Assenmacher, Lena Adam, Heike Trautmann).
  • Research Consortium – Unlocking Complex Resources through Lean Processing, Research Consortia Program, State Government of South Australia, 2017-2021 (several chief investigators and industry partners).
  • ARC Discovery Project "Bio-inspired Computing for Problems with Dynamically Changing Constraints", Australian Research Council, 2016-2018 (with Zbigniew Michalewicz, Tobias Friedrich, Marc Schoenauer)
  • Project "Modelling and optimisation of submerged buoys for improved ocean wave energy production", Interdisciplinary Research Fund, The University of Adelaide, 2015 (with Markus Wagner, Boyin Ding, Benjamin Cazzolato, Maziar Arjomandi).
  • ARC Discovery Project "Parameterised analysis of bio-inspired computing - from theory to high performing algorithms", Australian Research Council, 2014-2016 (with Tobias Friedrich)
  • ARC Discovery Project "Advanced planning systems for vertically integrated supply chain management", Australian Research Council, 2013-2015 (with Zbigniew Michalewicz and Adam Ghandar)
  • Project "Exploring the evolutionary diversity of Australia's marine snakes to develop a bio-mimetic sea snake robot", Interdisciplinary Research Fund, The University of Adelaide, 2013-2014 (with Lei Chen, Kate Sanders, Amy Watson, Gustavo Carneiro, Brett Goodman, Marc Jones)
  • Evolutionary Computation, semester 2, 2018
  • Grand Challenges in Computer Science, semester 2, 2017
  • Evolutionary Computation, semester 1, 2017
  • Mining Big Data, semester 1, 2017.
  • Evolutionary Computation, semester 2, 2016
  • Mining Big Data, semester 1, 2015.
  • Mining Big Data, semester 1, 2014.
  • Evolutionary Computation, semester 2, 2013.
  • Advanced Algorithms, semester 1, 2013.
  • Evolutionary Computation, semester 2, 2012.
  • Algorithm and Data Structure Analysis, semester 1, 2012.
  • Evolutionary Computation, semester 2, 2011.
  • Data Structures and Algorithms, semester 2, 2011.
  • Data Structures and Algorithms, semester 1, 2011.
  • Algorithms and Data Structures, winter semester, 2009/2010. (Saarland University, Germany)
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  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2021 Co-Supervisor Maximising throughput through intelligent online sensing and health monitoring Doctor of Philosophy Doctorate Full Time Mr Zeqi Li
    2021 Co-Supervisor Rapid updating of resource knowledge with sensor information including structures Doctor of Philosophy Doctorate Full Time Mr Sultan Abulkhair
    2020 Principal Supervisor Many Objective Evolutionary Diversity Optimization Doctor of Philosophy Doctorate Full Time Mr Adel Nikfarjam
    2020 Principal Supervisor Evolutionary Diversity Optimisation for Combinatorial Optimisation Problems Doctor of Philosophy Doctorate Full Time Mr Viet Anh Do
    2019 Co-Supervisor Towards Faster Scene Text Detection: A Comprehension Video Text Dataset and a Real-Time Video Text Detection Algorithm Doctor of Philosophy Doctorate Full Time Mr Chee Kheng Chng
    2018 Principal Supervisor Unlocking Complex Resources through Lean Processing Doctor of Philosophy Doctorate Full Time Miss Yue Xie
    2018 Principal Supervisor Optimisation of Supply Chains and Trusses Under Uncertainty Doctor of Philosophy Doctorate Full Time Mr Hirad Assimi
    2017 Principal Supervisor Automation and Coordination in Social Media Behaviour Doctor of Philosophy Doctorate Part Time Mr Derek Christopher Weber

  • Past Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2017 - 2020 Principal Supervisor Differential Evolution for Dynamic Constrained Continuous Optimisation Doctor of Philosophy Doctorate Full Time Mrs Maryam Hasani Shoreh
    2017 - 2020 Principal Supervisor Bio-Inspired Computing for Complex and Dynamic Constrained Problems Doctor of Philosophy Doctorate Full Time Mr Vahid Roostapour
    2015 - 2017 Principal Supervisor Ambulatory Monitoring using Passive RFID Technology Doctor of Philosophy Doctorate Full Time Mr Asanga Wickramasinghe
    2015 - 2018 Principal Supervisor Exact and Heuristic Approaches for Multi-component Optimisation Problems Doctor of Philosophy Doctorate Full Time Mr Junhua Wu
    2013 - 2016 Principal Supervisor Diversity Optimization and Parameterized Analysis of Heuristic Search Methods for Combinatorial Optimization Problems Doctor of Philosophy Doctorate Full Time Dr Wanru Gao
    2013 - 2017 Principal Supervisor Parameterised Complexity Analysis of Evolutionary Algorithms for Combinatorial Optimization Problems Doctor of Philosophy Doctorate Full Time Dr Mojgan Pourhassan
    2012 - 2015 Principal Supervisor Parameterized Analysis of Bio-inspired Computation and the Traveling Salesperson Problem Doctor of Philosophy Doctorate Full Time Mrs Samadhi Nethmini Nallaperuma
    2012 - 2016 Principal Supervisor Feature-Based Selection of Bio-Inspired Algorithms for Constrained Continuous Optimisation Doctor of Philosophy Doctorate Full Time Mr Shayan Poursoltan
    2011 - 2015 Co-Supervisor Particle swarm optimization: Theoretical analysis, Modifications, and Applications to Constrained Optimization Problems Doctor of Philosophy Doctorate Full Time Mr Mohammadreza Bonyadi
    2011 - 2013 Principal Supervisor Theory and Applications of Bio-Inspired Algorithms Doctor of Philosophy Doctorate Full Time APrf Markus Wagner
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  • Committee Memberships
    Date Role Committee Institution Country
    2016 - 2016 Chair General Chair Genetic and Evolutionary Computation Conference 2016 — United States
    2013 - 2013 Co-Chair Chair Foundations of Genetic Algorithms 2013 — Australia

  • Consulting/Advisories
    Date Institution Department Organisation Type Country
    2018 - ongoing Daitum — Business and professional Australia
    2015 - ongoing Complexica — Business and professional Australia
    2015 - ongoing Optimatics — Business and professional Australia
    2014 - 2016 Project SAGE Speed of Adaptation in Population Genetics and Evolutionary Computation — Scientific research United Kingdom

  • Editorial Boards
    Date Role Editorial Board Name Institution Country
    2017 - ongoing Associate Editor IEEE Transactions on Evolutionary Computation — —
    2014 - ongoing Associate Editor Evolutionary Computation — —
  • Position: Professor
  • Phone: 83134477
  • Email: frank.neumann@adelaide.edu.au
  • Fax: 8313 4366
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor 4
  • Room: 4 55
  • Org Unit: School of Computer Science
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