Publications

    Conference Papers

  1. A. Nikfarjam, Bossek, A. Neumann, F. Neumann (2021): Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.
    In: Foundations of Genetic Algorithms XVI, FOGA 2021, ACM Press.
    [CoRR abs/2108.05005]

  2. A. V. Do, M. Guo, A. Neumann, F. Neumann (2021): Analysis of evolutionary diversity optimisation for permutation problems.
    In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
    (Nominated for Best Paper Award in the track "Genetic Algorithms")
    [CoRR abs/2102.11469]

  3. W. Reid, A. Neumann, S. Ratcliffe, F. Neumann (2021): Advanced mine optimisation under uncertainty using Evolution.
    GECCO 2021 Workshop Industrial Applications of Metaheuristics
    In: Genetic and Evolutionary Computation Conference, GECCO 2021, Companion, ACM Press.
    [CoRR abs/2102.05235]

  4. Y. Xie, A. Neumann, F. Neumann (2021): Heuristic strategies for solving complex interacting large-scale stockpile blending problems.
    In: IEEE Congress on Evolutionary Computation, IEEE CEC 2021.
    [CoRR abs/2104.03440]

  5. A. Neumann, J. Bossek, F. Neumann (2021): Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions.
    In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
    [CoRR abs/2010.11486]

  6. A. Nikfarjam, Bossek, A. Neumann, F. Neumann (2021): Entropy-based evolutionary diversity optimisation for the traveling salesperson problem.
    In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
    [CoRR abs/2104.13538]

  7. Y. Xie, A. Neumann, F. Neumann, A. M. Sutton (2021): Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights.
    In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
    [CoRR abs/2102.05778]

  8. Y. Xie, A. Neumann, F. Neumann (2021): Heuristic strategies for complex interacting stockpile blending problems with chance constraints.
    In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
    [CoRR abs/2102.05303]

  9. J. Bossek, A. Neumann, F. Neumann (2021): Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms.
    In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
    [CoRR abs/2104.13133]

  10. J. Bossek, A. Neumann, F. Neumann (2021): Exact Counting and Sampling of Optima for the Knapsack Problem.
    In: The 15th Learning and Intelligent Optimization, LION 2021, Springer.
    [CoRR abs/2104.13133]

  11. A. Neumann, F. Neumann (2020): Human interactive EEG-based evolutionary image animation.
    In: IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2020.
    [CoRR abs/2104.13133]

  12. A. Neumann, F. Neumann (2020): Optimising chance-constrained submodular functions using evolutionary multi-objective algorithms.
    In: Parallel Problem Solving from Nature XVI, PPSN 2020.
    [CoRR abs/2006.11444]

  13. J. Bossek, A. Neumann, F. Neumann (2020): Optimising tours for the weighted traveling salesperson problem and the traveling thief problem: A structural comparison of solutions.
    In: Parallel Problem Solving from Nature XVI, PPSN 2020.
    [CoRR abs/2006.03260]

  14. J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann (2020): Evolving sampling strategies for one-shot optimization tasks.
    In: Parallel Problem Solving from Nature XVI, PPSN 2020.
    [CoRR abs/1912.08956]

  15. A. V. Do, J. Bossek, A. Neumann, F. Neumann (2020): Evolving diverse sets of tours for the travelling salesperson problem.
    In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
    [CoRR abs/2004.09188]

  16. Y. Xie, A. Neumann, F. Neumann (2020): Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problem.
    In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
    [CoRR abs/2004.03205]

  17. H. Assimi, O. Harper, Y. Xie, A. Neumann, F. Neumann (2020): Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives.
    In: 24th European Conference on Artificial Intelligence, ECAI 2020.
    [CoRR abs/2002.06766]

  18. V. Doskoc, T. Friedrich, A. Göbel, A. Neumann, F. Neumann, F. Quinzan (2020): Non-Monotone submodular maximization with multiple knapsacks in static and dynamic settings.
    In: 24th European Conference on Artificial Intelligence, ECAI 2020.
    Paper [CoRR abs/1911.06791]

  19. B. Doerr, C. Doerr, A. Neumann, F. Neumann, A. M. Sutton (2020): Optimization of chance-constrained submodular functions.
    In: Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020.
    [CoRR abs/1911.11451]

  20. J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann: (2019): Evolving diverse TSP instances by means of novel and creative mutation operators.
    In: Foundations of Genetic Algorithms XV, FOGA 2019, ACM Press.
    Paper

  21. A. Neumann, W. Gao, M. Wagner, F. Neumann: (2019): Evolutionary diversity optimization using multi-objective indicators.
    In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press. (Nominated for Best Paper Award in the track "Genetic Algorithms")
    [CoRR abs/1811.06804, Paper]

  22. Y. Xie, O. Harper, H. Assimi, A. Neumann, F. Neumann: (2019): Evolutionary algorithms for the chance-constrained knapsack problem.
    In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
    [CoRR abs/1902.04767]

  23. B. Alexander, D. Hin, A. Neumann, S. Ull Karim (2019): Evolving Pictures in Image Tran- sition Space.
    In: International Conference on Neural Information Processing, ICONIP 2019.
    [CoRR abs/1902.04767]

  24. V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2019): Pareto optimization for subset selection with dynamic cost constraints.
    In: Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019.
    [CoRR abs/1811.07806], AAAI version

  25. V. Roostapour, A. Neumann, F. Neumann (2018): On the performance of baseline evolutionary algorithms on the dynamic knapsack problem.
    In: Parallel Problem Solving from Nature XV, PPSN 2018.
    Paper

  26. A. Neumann, W. Gao, C. Doerr, F. Neumann, M. Wagner (2018): Discrepancy-based evolutionary diversity optimization.
    In: Genetic and Evolutionary Computation Conference, GECCO 2018, ACM Press.
    [CoRR abs/1802.05448]

  27. A. Neumann, C. Pyromallis, B. Alexander (2018): Evolution of Images with Diversity and Constraints Using a Generator Network.
    In: International Conference on Neural Information Processing, ICONIP 2018.
    [CoRR abs/1802.05448]

  28. A. Neumann, F. Neumann (2018): On the use of colour-based segmentation in evolutionary image composition.
    In: IEEE Congress on Evolutionary Computation, IEEE CEC 2018.
    Paper

  29. A. Neumann, Z. L. Szpak, W. Chojnacki, F. Neumann (2017): Evolutionary image composition using feature covariance matrices
    In: Genetic and Evolutionary Computation Conference, GECCO 2017, ACM Press.
    [CoRR abs/1703.03773]

  30. B. Alexander, J. Kortman, A. Neumann (2017): Evolution of Artistic Image Variants Through Feature Based Diversity Optimisation.
    In: Genetic and Evolutionary Computation Conference, GECCO 2017, ACM Press: 171-178, (CORE A).
    [CoRR abs/1703.03773]

  31. A. Neumann, B. Alexander, F. Neumann (2017): Evolutionary image transition using random walks.
    In: International Conference on Computational Intelligence in Music, Sound, Art and Design, EVOMUSART 2017.
    Paper

  32. M. El Yafrani, S. Chand, A. Neumann, B. Ahiod, M. Wagner (2017): Multi-objectiveness in the Single-objective Traveling Thief.
    Problem.
    In: Genetic and Evolutionary Computation Conference, GECCO (Companion) 2017, ACM Press.
    Paper

  33. W. Li, E. Ozcan, J. R. Drake, A. Neumann, M. Wagner (2017): A Modified Indicator-based Evolutionary Algorithm (mIBEA).
    In: IEEE Congress on Evolutionary Computation, CEC 2017, IEEE Press.
    Paper

  34. A. Neumann, B. Alexander, F. Neumann (2016): The evolutionary process of image transition in conjunction with box and strip mutation.
    In: International Conference on Neural Information Processing, ICONIP 2016.
    [CoRR abs/1608.01783]

    35. Journal Papers

  35. V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2021): Pareto optimization for subset selection with dynamic cost constraints.
    Artificial Intelligence.
    [CoRR abs/1811.07806]

  36. A. Neumann, B. Alexander, F. Neumann (2020): Evolutionary image transition and painting using random walks.
    Evolutionary Computation Journal.
    [CoRR abs/2003.01517, Videos]

  37. A. Neumann, F. Neumann, T. Friedrich (2019): Quasi-random Agents for Image Transition and Animation.
    Australian Journal of Intelligent Information Processing Systems, Volume 16, Number 1, 10-18.
    Available: [CoRR abs/1710.07421]

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