Professor Frank Neumann
ARC Externally-Funded Research Fellow (E)
School of Computer Science
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
Frank Neumann is a professor and the leader of the Optimisation and Logistics group at the University of Adelaide and an Honorary Professorial Fellow at the University of Melbourne. His current position is funded by the Australian Research Council through a Future Fellowship and focuses on AI-based optimisation methods for problems with stochastic constraints. Frank has been the general chair of the ACM GECCO 2016 and co-organised ACM FOGA 2013 in Adelaide. He is an Associate Editor of the journals "Evolutionary Computation" (MIT Press) and ACM Transactions on Evolutionary Learning and Optimization. In his work, he considers algorithmic approaches 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 cybersecurity, renewable energy, logistics, and mining.
- My Research
- Career
- Publications
- Grants and Funding
- Teaching
- Supervision
- Professional Activities
- Contact
- artificial intelligence
- bio-inspired computing
- optimization
- renewable energy
- supply chain management
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Appointments
Date Position Institution name 2016 - ongoing 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 2022 Roostapour, V., Neumann, A., & Neumann, F. (2022). Single- and multi-objective evolutionary algorithms for the knapsack problem with dynamically changing constraints. Theoretical Computer Science, 924, 129-147.
Scopus12022 Nikfarjam, A., Neumann, A., Bossek, J., & Neumann, F. (2022). Co-evolutionary Diversity Optimisation for the Traveling Thief Problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13398 LNCS, 237-249.
2022 Nikfarjam, A., Viet Do, A., & Neumann, F. (2022). Analysis of Quality Diversity Algorithms for the Knapsack Problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13399 LNCS, 413-427.
2022 Nikfarjam, A., Moosavi, A., Neumann, A., & Neumann, F. (2022). Computing High-Quality Solutions for the Patient Admission Scheduling Problem Using Evolutionary Diversity Optimisation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13398 LNCS, 250-264.
2022 Nikfarjam, A., Neumann, A., & Neumann, F. (2022). Evolutionary diversity optimisation for the traveling thief problem. GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference, 749-756.
2022 Neumann, A., Antipov, D., & Neumann, F. (2022). Coevolutionary Pareto diversity optimization. GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference, 832-839.
2022 Neumann, A., Xie, Y., & Neumann, F. (2022). Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13398 LNCS, 294-307.
2022 Dumuid, D., Olds, T., Wake, M., Lund Rasmussen, C., Pedišić, Ž., Hughes, J. H., . . . Stanford, T. (2022). Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures. PLoS One, 17(9), e0272343-1-e0272343-16.
2022 Nikfarjam, A., Neumann, A., & Neumann, F. (2022). On the use of quality diversity algorithms for the traveling thief problem. GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference, 260-268.
2022 Assimi, H., Koch, B., Garcia, C., Wagner, M., & Neumann, F. (2022). Run-of-mine stockyard recovery scheduling and optimisation for multiple reclaimers. Proceedings of the ACM Symposium on Applied Computing, 1074-1083.
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Niching-based evolutionary diversity optimization for the traveling salesperson problem. GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference, 684-693.
2022 Bossek, J., & Neumann, F. (2022). Exploring the feature space of TSP instances using quality diversity. GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference, 186-194.
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, 089443932110122.
Scopus3 WoS32021 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.
Scopus12021 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.
Scopus1 WoS12021 Weber, D., & Neumann, F. (2021). Amplifying influence through coordinated behaviour in social networks. Social Network Analysis and Mining, 11(1), 1-42.
Scopus72021 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.
Scopus1 WoS52021 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: an international journal in computer science, 83, 906-939.
2021 Neumann, A., Alexander, B., & Neumann, F. (2021). Evolutionary Image Transition and Painting Using Random Walks. Evolutionary Computation, 28(4), 643-675.
2021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints.. arXiv, abs/2102.05303, 1079-1087.
Scopus3 WoS12021 Doerr, B., & Neumann, F. (2021). A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization. ACM Transactions on Evolutionary Learning and Optimization, 1(4), 16-1-16-43.
2020 Neumann, A., Bossek, J., & Neumann, F. (2020). Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems.. CoRR, abs/2010.11486. 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.
2020 Neumann, A., Alexander, B., & Neumann, F. (2020). Evolutionary image transition and painting using random walks. Evolutionary Computation, 28(4), 643-675.
Scopus8 WoS62020 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.
Scopus4 WoS52020 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.
Scopus4 WoS32020 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.
Scopus2 WoS22020 Friedrich, T., Kötzing, T., Lagodzinski, J. A. G., 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.
Scopus9 WoS72020 Do, A. V., & Neumann, F. (2020). Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints.. CoRR, abs/2012.08738. 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.
Scopus12 WoS92019 Kerschke, P., Hoos, H., Neumann, F., & Trautmann, H. (2019). Automated algorithm selection: survey and perspectives. Evolutionary Computation, 27(1), 3-45.
Scopus148 WoS99 Europe PMC22019 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.
Scopus4 WoS12019 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.
Scopus12019 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.
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.
Scopus2 WoS22019 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.
Scopus6 WoS12017 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.
Scopus15 WoS8 Europe PMC12017 Polyakovskiy, S., & Neumann, F. (2017). The Packing While Traveling Problem. European Journal of Operational Research, 258(2), 424-439.
Scopus8 WoS72017 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.
Scopus6 WoS22017 Doerr, B., Neumann, F., & Sutton, A. (2017). Time Complexity Analysis of Evolutionary Algorithms on Random Satisfiable k-CNF Formulas. Algorithmica, 78(2), 561-586.
Scopus12 WoS102016 Hoos, H. H., Neumann, F., & Trautmann, H. (2016). Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412).. Dagstuhl Reports, 6, 33-74.
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.
2016 Keating, C. B., & Ireland, V. (2016). Editorial. International Journal of System of Systems Engineering, 7(1/2/3), 1-21.
Scopus62016 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.
Scopus1 WoS12016 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.
Scopus12 WoS8 Europe PMC12016 Kaufmann, P., Kramer, O., Neumann, F., & Wagner, M. (2016). Optimization methods in renewable energy systems design. Renewable Energy, 87, 835-836.
Scopus3 WoS32015 Friedrich, T., & Neumann, F. (2015). Maximizing submodular functions under matroid constraints by evolutionary algorithms. Evolutionary Computation, 23(4), 543-558.
Scopus42 WoS322015 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.
Scopus232015 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.
Scopus9 WoS8 Europe PMC12015 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.
Scopus6 WoS52015 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.
Scopus8 WoS72015 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.
Scopus22 WoS192015 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.
Scopus10 WoS82014 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.
2014 Sutton, A. M., Neumann, F., & Nallaperuma, S. (2014). Parameterized runtime analyses of evolutionary algorithms for the planar Euclidean traveling salesperson problem. Evolutionary Computation, 22(4), 595-628.
Scopus24 WoS16 Europe PMC12013 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.
Scopus17 WoS152013 Kratsch, S., & Neumann, F. (2013). Fixed-parameter evolutionary algorithms and the vertex cover problem. Algorithmica, 65(4), 754-771.
Scopus49 WoS352013 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.
Scopus26 WoS202013 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.
Scopus78 WoS742013 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.
Scopus71 WoS562013 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.
Scopus61 WoS392013 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.
Scopus14 WoS102012 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.
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.
Scopus42 WoS302011 Doerr, B., Eremeev, A., Neumann, F., Theile, M., & Thyssen, C. (2011). Evolutionary algorithms and dynamic programming. Theoretical Computer Science, 412(43), 6020-6035.
Scopus14 WoS82011 Friedrich, T., Horoba, C., & Neumann, F. (2011). Illustration of fairness in evolutionary multi-objective optimization. Theoretical Computer Science, 412(17), 1546-1556.
Scopus9 WoS72011 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.
Scopus24 WoS182011 Neumann, F., Reichel, J., & Skutella, M. (2011). Computing minimum cuts by randomized search heuristics. Algorithmica (New York), 59(3), 323-342.
Scopus21 WoS202010 Friedrich, T., & Neumann, F. (2010). When to use bit-wise neutrality. Natural Computing, 9(1), 283-294.
Scopus1 WoS12010 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.
Scopus83 WoS63 Europe PMC52010 Doerr, B., Neumann, F., & Wegener, I. (2010). Algorithmica (New York): Editorial. Algorithmica (New York), 57(1), 119-120.
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.
Scopus1 WoS12010 Friedrich, T., Hebbinghaus, N., & Neumann, F. (2010). Plateaus can be harder in multi-objective optimization. Theoretical Computer Science, 411(6), 854-864.
Scopus8 WoS72010 Neumann, F., & Witt, C. (2010). Ant Colony Optimization and the minimum spanning tree problem. Theoretical Computer Science, 411(25), 2406-2413.
Scopus49 WoS402009 Doerr, B., & Neumann, F. (2009). In Memoriam: Ingo Wegener. Algorithmica (New York), 58(3), 1-2.
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.
Scopus652009 Neumann, F., & Witt, C. (2009). Runtime analysis of a simple ant colony optimization algorithm. Algorithmica, 54(2), 243-255.
Scopus68 WoS462009 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.
Scopus33 WoS252009 Friedrich, T., Hebbinghaus, N., & Neumann, F. (2009). Comparison of simple diversity mechanisms on plateau functions. Theoretical Computer Science, 410(26), 2455-2462.
Scopus12 WoS82009 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.
Scopus64 WoS522009 Jansen, T., Schmidt, M., Sudholt, D., Witt, C., & Zarges, C. (2009). In Memoriam: Ingo Wegener. EVOLUTIONARY COMPUTATION, 17(1), 1-2.
2008 Neumann, F. (2008). Expected runtimes of evolutionary algorithms for the Eulerian cycle problem. Computers & Operations Research, 35(9), 2750-2759.
Scopus33 WoS232007 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.
Scopus65 WoS532007 Neumann, F., & Wegener, I. (2007). Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theoretical Computer Science, 378(1), 32-40.
Scopus163 WoS1222007 Doerr, B., Hebbinghaus, N., & Neumann, F. (2007). Speeding Up Evolutionary Algorithms through Asymmetric Mutation Operators. Evolutionary Computation, 15(4), 401-410.
Scopus32 WoS26 Europe PMC22006 Neumann, F., & Wegener, I. (2006). Minimum spanning trees made easier via multi-objective optimization. Natural Computing, 5(3), 305-319.
Scopus98Guo, M., Li, J., Neumann, A., Neumann, F., & Nguyen, H. (n.d.). Practical Fixed-Parameter Algorithms for Defending Active Directory
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Books
Year Citation 2010 Neumann, F., & Witt, C. (2010). Bioinspired computation in combinatorial optimization : algorithms and their computational complexity. Berlin: Springer.
2010 Neumann, F., & Witt, C. (2010). Bioinspired Computation in Combinatorial Optimization. Springer.
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Book Chapters
Year Citation 2022 Assimi, H., Neumann, F., Wagner, M., & Li, X. (2022). Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. In L. P. Caceres, & S. Verel (Eds.), Evolutionary Computation in Combinatorial Optimization (Vol. 13222 LNCS, pp. 111-126). SPRINGER INTERNATIONAL PUBLISHING AG.
2022 Shi, F., Yan, X., & Neumann, F. (2022). Runtime Analysis of Simple Evolutionary Algorithms for the Chance-Constrained Makespan Scheduling Problem. In Lecture Notes in Computer Science (Vol. 13399 LNCS, pp. 526-541). Springer International Publishing.
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.
Scopus42020 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.
Scopus112018 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.
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.
Scopus82012 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.
WoS52011 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.
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.
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.
Scopus72009 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.
Scopus182008 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.
Scopus282006 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.
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.
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.
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.
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Conference Papers
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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.
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 Abstracts of the Genetic and Evolutionary Computation Conference Companion (GECCO 2019). Prague, The Czech Republic: Association for Computing Machinery (ACM).
Scopus1 WoS12018 Neumann, A., & Neumann, F. (2018). Evolutionary computation for digital art. Poster session presented at the meeting of Abstracts of the Genetic and Evolutionary Computation Conference Companion (GECCO 2018). Kyoto, Japan: Association for Computing Machinery.
Scopus22016 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 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). 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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). 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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). 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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). 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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.
Scopus8 WoS62016 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.
Scopus12 WoS92011 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.
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.
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Working Paper
Year Citation 2018 Neumann, A., Gao, W., Wagner, M., & Neumann, F. (2018). Evolutionary Diversity Optimization Using Multi-Objective Indicators.. 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.
Scopus11 WoS62016 Neumann, F., & Poursoltan, S. (2016). Feature-based algorithm selection for constrained continuous optimisation. IEEE.
Scopus3 WoS32016 Neumann, A., Alexander, B., & Neumann, F. (2016). The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation. 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.
Scopus32020 Doerr, B., Doerr, C., Neumann, A., Neumann, F., & Sutton, A. M. (2020). Optimization of Chance-Constrained Submodular Functions.. AAAI Press. 2019 Xie, Y., Harper, O., Assimi, H., Neumann, A., & Neumann, F. (2019). Evolutionary algorithms for the chance-constrained knapsack problem.. 2019 Neumann, A., Neumann, F., & Friedrich, T. (2019). Quasi-random Image Transition and Animation.. 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 2022 Principal Supervisor Bio-inspired Computing for Problems with Chance Constraints Doctor of Philosophy Doctorate Full Time Mr Xiankun Yan 2022 Principal Supervisor Evolutionary Algorithms for Solving Chance Constrained Combinatorial Optimization Problems Doctor of Philosophy Doctorate Full Time Miss Saba Sadeghi Ahouei 2022 Co-Supervisor Bio-inspired Computing for Problems with Chance Constraints Doctor of Philosophy Doctorate Full Time Mrs Kokila Perera 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 2021 Principal Supervisor Integration of Grinding Circuit Sensors Including Ultrasonics for Particle Size Distributions to Maximise Mill Throughput Doctor of Philosophy Doctorate Full Time Mrs Zahra Ghasemi 2021 Principal Supervisor Is complexity an evolutionary response to selection change? Master of Philosophy Master Part Time Mr Gabor Zoltai 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 Optimisation of Supply Chains and Trusses Under Uncertainty Doctor of Philosophy Doctorate Part Time Mr Hirad Assimi -
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2018 - 2021 Principal Supervisor Bio-Inspired Computing for Chance-Constrained Combinatorial Optimisation Problems Doctor of Philosophy Doctorate Full Time Miss Yue Xie 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 2017 - 2022 Principal Supervisor Towards Exposing Coordinating Inauthentic Groups on Social Media Doctor of Philosophy Doctorate Part Time Mr Derek Christopher Weber 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
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