Dr Frank Neumann

Frank Neumann
Professor
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences

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 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 renewable energy, logistics, and mining.

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Dr Frank Neumann

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 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 renewable energy, logistics, and mining.

✓ Eligible to supervise Masters and PhD — email supervisor to discuss availability.

  • bio-inspired computing
  • combinatorial optimization
  • multi-objective optimization
  • renewable energy
  • supply chain management

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

Journals

Year Citation
2018 Chin, T. -J., Cai, Z., & Neumann, F. (2018). Robust Fitting in Computer Vision: Easy or Hard?. CoRR, abs/1802.06464.
2018 Wu, J., Polyakovskiy, S., Wagner, M., & Neumann, F. (2018). Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem.. CoRR, abs/1802.02434.
2018 Ameca-Alducin, M. Y., Hasani-Shoreh, M., Blaikie, W., Neumann, F., & Mezura-Montes, E. (2018). A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems.. CoRR, abs/1802.05825.
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
2017 Polyakovskiy, S., & Neumann, F. (2017). The Packing While Traveling Problem.. European Journal of Operational Research, 258, 424-439.
2017 Neumann, F., Polyakovskiy, S., Skutella, M., Stougie, L., & Wu, J. (2017). A Fully Polynomial Time Approximation Scheme for Packing While Traveling.. CoRR, abs/1702.05217.
2017 Polyakovskiy, S., & Neumann, F. (2017). The Packing While Traveling Problem. Unknown Journal, 424-439.
DOI Scopus3 WoS1
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
2016 Nallaperuma, S., Neumann, F., & Sudholt, D. (2016). Expected fitness gains of randomized search heuristics for the traveling salesperson problem. Evolutionary Computation, OnlinePub(4), 1-33.
DOI Europe PMC1
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
2016 Corus, D., Lehre, P. K., Neumann, F., & Pourhassan, M. (2016). A Parameterised Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms.. Evolutionary Computation, 24, 183-203.
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 Hoos, H., Neumann, F., & Trautmann, H. (2016). Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412).. Dagstuhl Reports, 6, 33-74.
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 Scopus1 WoS1
2016 Kaufmann, P., Kramer, O., Neumann, F., & Wagner, M. (2016). Optimization methods in renewable energy systems design. Renewable Energy, 87, 835-836.
DOI Scopus1 WoS1
2015 Friedrich, T., & Neumann, F. (2015). Maximizing submodular functions under matroid constraints by evolutionary algorithms. Evolutionary Computation, 23(4), 543-558.
DOI
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, 18-1-18-16.
DOI
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 Scopus3 WoS1
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 Scopus2 WoS2
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 Scopus2 WoS1
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 Scopus6 WoS7
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 Scopus2 WoS1
2014 Neumann, F., Doerr, B., Lehre, P., & Haddow, P. (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 Scopus5 WoS3
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.
2013 Doerr, B., Eremeev, A. V., Neumann, F., Theile, M., & Thyssen, C. (2013). Evolutionary Algorithms and Dynamic Programming. CoRR, abs/1301.4096.
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 Scopus11 WoS9
2013 Kratsch, S., & Neumann, F. (2013). Fixed-parameter evolutionary algorithms and the vertex cover problem. Algorithmica, 65(4), 754-771.
DOI Scopus22 WoS15
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 Scopus10 WoS6
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 Scopus42 WoS39
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 Scopus25 WoS19
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 Scopus25 WoS13
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 Scopus7 WoS7
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 Scopus25 WoS19
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.
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 Scopus6 WoS4
2011 Friedrich, T., Horoba, C., & Neumann, F. (2011). Illustration of fairness in evolutionary multi-objective optimization. Theoretical Computer Science, 412(17), 1546-1556.
DOI Scopus5 WoS3
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 Scopus12 WoS9
2011 Neumann, F., Reichel, J., & Skutella, M. (2011). Computing minimum cuts by randomized search heuristics. Algorithmica (New York), 59(3), 323-342.
DOI Scopus11 WoS11
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 Scopus42 WoS30 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 Scopus4 WoS4
2010 Neumann, F., & Witt, C. (2010). Ant Colony Optimization and the minimum spanning tree problem. Theoretical Computer Science, 411(25), 2406-2413.
DOI Scopus31 WoS27
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 Scopus45
2009 Neumann, F., & Witt, C. (2009). Runtime analysis of a simple ant colony optimization algorithm. Algorithmica, 54(2), 243-255.
DOI Scopus47 WoS30
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 Scopus18 WoS13
2009 Friedrich, T., Hebbinghaus, N., & Neumann, F. (2009). Comparison of simple diversity mechanisms on plateau functions. Theoretical Computer Science, 410(26), 2455-2462.
DOI Scopus9 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 Scopus44 WoS34
2008 Neumann, F. (2008). Expected runtimes of evolutionary algorithms for the Eulerian cycle problem. Computers & Operations Research, 35(9), 2750-2759.
DOI Scopus24 WoS17
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 Scopus38 WoS31
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 Scopus105 WoS80
2007 Doerr, B., Hebbinghaus, N., & Neumann, F. (2007). Speeding Up Evolutionary Algorithms through Asymmetric Mutation Operators. Evolutionary Computation, 15(4), 401-410.
DOI Scopus20 WoS21 Europe PMC2
2006 Neumann, F., & Wegener, I. (2006). Minimum spanning trees made easier via multi-objective optimization. Natural Computing, 5(3), 305-319.
DOI Scopus72

Books

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

Book Chapters

Year Citation
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).
DOI Scopus2
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 WoS3
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 Scopus4
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 Scopus12
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

Conference Papers

Year Citation
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 (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10784 LNCS (pp. 832-847). Springer.
DOI
2018 Neumann, A., Gao, W., Doerr, C., Neumann, F., & Wagner, M. (2018). Discrepancy-based Evolutionary Diversity Optimization.. In CoRR Vol. abs/1802.05448.
2017 Neumann, F. (2017). Parameterized analysis of bio-inspired computing.. In SSCI (pp. 1-3). IEEE.
2017 Neumann, A., Szpak, Z. L., Chojnacki, W., & Neumann, F. (2017). Evolutionary image composition using feature covariance matrices.. In P. A. Bosman (Ed.), GECCO (pp. 817-824). ACM.
DOI
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
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.), FOGA 2017 - Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 45-54). ACM.
DOI Scopus1
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
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 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 1407-1414). New York, NY, USA: Association for Computing Machinery (ACM).
DOI
2017 Osuna, E., Neumann, F., Gao, W., & Sudholt, D. (2017). Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 553-560). Berlin, Germany: Association for Computing Machinery (ACM).
DOI
2017 Friedrich, T., & Neumann, F. (2017). What's hot in evolutionary computation. In S. P. Singh, & S. Markovitch (Eds.), 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 5064-5066). AAAI Press.
Scopus1
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).
DOI
2017 Pourhassan, M., Roostapour, V., & Neumann, F. (2017). Improved runtime analysis of RLS and (1+1) EA for the dynamic vertex cover problem.. In SSCI (pp. 1-6). IEEE.
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). Springer.
2017 Neumann, A., Szpak, Z., 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). Berlin, Germany: ACM.
DOI
2017 Neumann, A., Alexander, B., & Neumann, F. (2017). Evolutionary image transition using random walks. In Proccedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017) Vol. 10198 LNCS (pp. 230-245). Cham, Switzerland: Springer.
DOI Scopus2
2016 Gao, W., Nallaperuma, S., & Neumann, F. (2016). Feature-based diversity optimization for problem instance classification. In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016) Vol. 9921 LNCS (pp. 869-879). Edinburgh, UK: Springer International Publishing.
DOI Scopus1
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 Scopus2
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 Scopus5 WoS1
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
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 Scopus5 WoS2
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 Scopus2
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
2016 Neumann, A., Alexander, B., & Neumann, F. (2016). The evolutionary process of image transition in conjunction with box and strip mutation. In Processings of the 23rd International Conference on Neural Information Processing (ICONIP 2016), as published in Lecture Notes in Computer Science Vol. 9949 LNCS (pp. 261-268). Kyoto, Japan: Springer International Publishing AG.
DOI Scopus3 WoS1
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.. In T. Friedrich, F. Neumann, & A. M. Sutton (Eds.), GECCO (pp. 1029-1036). ACM.
DOI
2015 Neumann, F., & Sutton, A. (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.
DOI
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
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.
Scopus1
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 Scopus1 WoS1
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 Scopus1 WoS1
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 Scopus1 WoS1
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 Scopus1 WoS1
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 Scopus1 WoS1
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 Scopus5 WoS3
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 Scopus4 WoS4
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 Scopus2 WoS2
2014 Polyakovskiy, S., Bonyadi, M. R., 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: ACM.
DOI Scopus34 WoS15
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 Scopus3 WoS2
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 Scopus4 WoS2
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 Scopus7 WoS3
2014 Neumann, F., & Sutton, A. (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.
DOI
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 Scopus4 WoS5
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 Scopus3 WoS2
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 Scopus4
2013 Neumann, F., & Jong, K. (2013). Foreword. In FOGA 2013 - Proceedings of the 12th ACM Workshop on Foundations of Genetic Algorithms.
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.
DOI
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 Scopus5 WoS3
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 Scopus12
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 Scopus16 WoS17
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 Scopus23 WoS18
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.
DOI Scopus3
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 Scopus1 WoS2
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 Scopus4 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.
DOI Scopus5
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.
DOI Scopus11
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.
DOI Scopus5
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 Scopus21
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
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 Scopus19 WoS10
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 Scopus4 WoS2
2012 Kotzing, T., Sutton, A., Neumann, F., & O'Reilly, U. M. (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.
DOI Scopus9 WoS4
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.
DOI Scopus14 WoS8
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.
Scopus9
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.
DOI Scopus12
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 Scopus12 WoS8
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.
DOI Scopus21 WoS11
2011 Durrett, G., Neumann, F., & O'Reilly, U. M. (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 Scopus20
2011 Wagner, M., Neumann, F., Veeramachaneni, K., & O'Reilly, U. M. (2011). Optimizing the Layout of 1000 Wind Turbines. In European Wind Energy Association 2011 Scientific Proceedings (pp. 205-209). Brussels, Belgium: UWEA.
Scopus28
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.
DOI Scopus48
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 Scopus18 WoS14
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 Scopus9 WoS5
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.
DOI Scopus13
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.
DOI Scopus25
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.
DOI Scopus5
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 Scopus11 WoS8
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 Scopus11 WoS7
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.
DOI Scopus15 WoS14
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 Scopus11 WoS7
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.
DOI Scopus43 WoS32
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.
DOI
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.
DOI
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.
DOI
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 Scopus9 WoS2
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 Scopus37
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.
DOI Scopus6
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.
DOI Scopus36
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 Scopus18 WoS10
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 Scopus7
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.
DOI Scopus31
2009 Jansen, T., & Neumann, F. (2009). Computational complexity and evolutionary computation.. In F. Rothlauf (Ed.), GECCO (Companion) (pp. 3157-3184). ACM.
DOI
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.
DOI
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 Scopus22 WoS17
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 Scopus22 WoS19
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 Scopus26 WoS27
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 Scopus37
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 Scopus23
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.
DOI
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.
DOI
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.
DOI
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 Scopus6 WoS4
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 Scopus16 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 Scopus11
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.
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
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 Scopus9 WoS5
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 Scopus62 WoS30
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 Scopus40 WoS12
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 Scopus18 WoS5
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 Scopus35 WoS11
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 WoS2
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 Scopus15 WoS12
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
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
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.
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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).
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2005 Neumann, F., & Wegener, I. (2005). Minimum spanning trees made easier via multi-objective optimization. In H. Beyer (Ed.), GECCO 2005 - Genetic and Evolutionary Computation Conference (pp. 763-769). Washington, DC: ASSOC COMPUTING MACHINERY.
DOI Scopus25 WoS6
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 Scopus32 WoS42
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. 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.
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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. Lozano, J. Smith, J. MereloGuervos, J. Bullinaria, . . . H. 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.
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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 Scopus19 WoS9

Conference Items

Year Citation
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.
DOI
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.
DOI
2016 Abdolmaleki, A., Lau, N., Reis, L. P., & Neumann, G. (2016). Contextual Stochastic Search.. Poster session presented at the meeting of GECCO (Companion). ACM.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
2016 Vostinar, A. E., Zaman, L., & Ofria, C. (2016). What Factors Drive the Evolution of Mutualism?. Poster session presented at the meeting of GECCO (Companion). ACM.
DOI
2016 Volz, V., Rudolph, G., & Naujoks, B. (2016). Demonstrating the Feasibility of Automatic Game Balancing.. Poster session presented at the meeting of GECCO. ACM.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
2016 Merelo, J. J., 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.
DOI
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.
DOI
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.
DOI
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.
DOI
2016 Larson, A., Bernatskiy, A., Cappelle, C., Livingston, K. R., Livingston, N., Long, J. H., . . . Bongard, J. C. (2016). Recombination Hotspots Promote the Evolvability of Modular Systems.. Poster session presented at the meeting of GECCO (Companion). ACM.
DOI
2016 Scott, E., & De Jong, K. (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 Scopus3 WoS1
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.
DOI
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.
DOI
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 Scopus5 WoS3
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
2016 Neumann, A., Alexander, B., & Neumann, F. (2016). The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation.
2016 Neumann, A., Alexander, B., & Neumann, F. (2016). Evolutionary Image Transition Based on Theoretical Insights of Random Processes..
2016 Pourhassan, M., Shi, F., & Neumann, F. (2016). Parameterized analysis of multi-objective evolutionary algorithms and the weighted vertex cover problem. SPRINGER INT PUBLISHING AG.
DOI
2016 Neumann, F., & Poursoltan, S. (2016). Feature-based algorithm selection for constrained continuous optimisation. IEEE.
DOI
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
2017 Neumann, A., Neumann, F., & Friedrich, T. (2017). Quasi-random Agents for Image Transition and Animation..
  • 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)

Current Higher Degree by Research Supervision (University of Adelaide)

Date Role Research Topic Program Degree Type Student Load Student Name
2017 Principal Supervisor Bio-Inspired computing for problems with Dynamically changing constraints Doctor of Philosophy Doctorate Full Time Mr Vahid Roostapour
2017 Principal Supervisor Optimization of Energy Systems with Dynamic Constraints Doctor of Philosophy Doctorate Full Time Mrs Maryam Hasani Shoreh
2017 Principal Supervisor Automation and Coordination in Social Media Behaviour Doctor of Philosophy Doctorate Full Time Mr Derek Christopher Weber
2015 Principal Supervisor Feature-based Analysis of Bio-inspired Algorithms Doctor of Philosophy Doctorate Full Time Mr Junhua Wu
2011 Co-Supervisor A Visualised Provenance A Data Management Study on Tracking and Managing Information in The Cloud Using Digital Forensics Doctor of Philosophy Doctorate Full Time Mr Johan Scholtz

Past Higher Degree by Research Supervision (University of Adelaide)

Date Role Research Topic Program Degree Type Student Load Student Name
2015 - 2017 Principal Supervisor Ambulatory Monitoring using Passive RFID Technology Doctor of Philosophy Doctorate Full Time Mr Asanga Wickramasinghe
2013 - 2016 Principal Supervisor Diversity Optimization and Parameterized Analysis of Heuristic Search Methods for Combinatorial Optimization Problems Doctor of Philosophy Doctorate Full Time Miss Wanru Gao
2013 - 2017 Principal Supervisor Parameterised Complexity Analysis of Evolutionary Algorithms for Combinatorial Optimization Problems Doctor of Philosophy Doctorate Full Time Mrs 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 Dr Markus Wagner

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

Editorial Boards

Date Role Editorial Board Name Institution Country
2017 - ongoing Associate Editor IEEE Transactions on Evolutionary Computation
2014 - ongoing Associate Editor Evolutionary Computation

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
Position
Professor
Phone
83134477
Email
frank.neumann@adelaide.edu.au
Fax
8313 4366
Campus
North Terrace
Building
Ingkarni Wardli, floor 4
Room Number
4 55
Org Unit
School of Computer Science

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