Project "Bio-inspired computing for problems with chance constraints"

Bio-inspired algorithms have successfully been applied to a wide range of optimisation problems. Uncertainties in real-world applications can lead to critical failures of production schedules or safe critical systems. Chance constraints model such uncertainties and allow to limit the possibility of such failures. This project builds up the area of bio-inspired computing for problems with chance constraints. It develops high performing bio- inspired algorithms for stochastic problems where the constraints can only be violated with a small probability. The outcomes will lead to more effective and reliable optimisation methods for complex planning processes in areas of national priority such as mining and manufacturing. The work has been supported by the Australian Research Council through a Future Fellowship.

Selected Key Publications

Journal Papers

• F. Neumann, Carsten Witt (2024): Runtime analysis of single- and multi-objective evolutionary algorithms for chance constrained optimization problems with normally distributed random variables.
  Evolutionary Computation.
  [CoRR abs/2109.05799, Final version]

• B. Doerr, F. Neumann (2021): A survey on recent progress in the theory of evolutionary algorithms for discrete optimization.
  ACM Transactions on Evolutionary Learning and Optimization.
  [CoRR abs/2006.16709, Final version]

Conference Papers

• F. Neumann, G. Rudolph (2024): Archive-based single-objective evolutionary algorithms for submodular optimization.
  In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
  [CoRR abs/2406.13414]

• K. Perera, F. Neumann, A. Neumann (2024): Multi-objective evolutionary approaches for the knapsack problem with stochastic profits.
  In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
  [CoRR abs/2303.01695]

• F. Neumann, C. Witt (2024): Sliding window 3-objective Pareto optimization for problems with chance constraints.
  In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
  [CoRR abs/2406.04899]

• T. C. Pathirage Don, A. Neumann, F. Neumann: The chance constrained travelling thief problem: problem formulations and algorithms.
  In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
  [Final version (open access)]

• I. Hewa Pathiranage, F. Neumann, D. Antipov, A. Neumann: Effective 2- and 3-objective MOEA/D approaches for the chance constrained knapsack problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
  [Final version (open access)]

• I. Hewa Pathiranage, F. Neumann, D. Antipov, A. Neumann: Using 3-objective evolutionary algorithms for the dynamic chance constrained knapsack problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
  [Final version (open access)]

• X. Yan, A. Neumann, F. Neumann: Sampling-based Pareto optimization for chance-constrained monotone submodular problems.
  In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
  [Final version (open access)]

• S. Sadeghi Ahouei, J. de Nobel, A. Neumann, T. Bäck, F. Neumann: Evolving reliable differentiating constraints for the chance-constrained maximum coverage problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
  [Final version (open access)]

• A. Opris, D. Dang, F. Neumann, Sudholt: Runtime analyses of NSGA-III on many-objective problems.
  In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
  [Final version (open access)]

• V. A. Do, A. Neumann, F. Neumann, A. M. Sutton (2023): Rigorous runtime analysis of MOEA/D for solving multi-objective minimum weight base problems.
  In: Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS 2023.
  [CoRR abs/2306.03409]

• X. Yan, V. A. Do, F. Shi, X. Qin, F. Neumann: (2023): Optimizing chance constrained submodular problems with variable uncertainties.
  In: 26th European Conference on Artificial Intelligence, ECAI 2023.
  [CoRR abs/2309.14359, Paper]

• F. Neumann, C. Witt: (2023): Fast Pareto optimization using sliding window selection.
  In: 26th European Conference on Artificial Intelligence, ECAI 2023.
  [CoRR abs/2305.07178, Paper]

• F. Neumann, C. Witt: (2023): 3-objective Pareto optimization for problems with chance constraints.
  In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
  [CoRR abs/2304.08774]

• F. Neumann, A. Neumann, C. Qian, V. A. Do, J. de Nobel, D. Vermetten, S. Sadeghi Ahouei, F. Ye, H. Wang, T. Bäck : (2023): Benchmarking algorithms for submodular optimization problems using IOHProfiler.
  In: IEEE Congress on Evolutionary Computation, IEEE CEC 2023.
  [CoRR abs/2302.01464]

• M. Stimson, W. Reid, A. Neumann, S. Ratcliffe, F. Neumann: (2023): Improving confidence in evolutionary mine scheduling via uncertainty discounting.
  In: IEEE Congress on Evolutionary Computation, IEEE CEC 2023.
  [CoRR abs/2305.17957]

• A. Neumann, X. Yie, F. Neumann (2022): Evolutionary algorithms for limiting the effect of uncertainty for the knapsack problem with stochastic profits.
  In: Parallel Problem Solving from Nature XVII, PPSN 2022.
  [CoRR abs/2204.05597]

• F. Shi, X. Yan, F. Neumann (2022): Runtime analysis of simple evolutionary algorithms for the chance-constrained makespan scheduling problem.
  In: Parallel Problem Solving from Nature XVII, PPSN 2022.
  [CoRR abs/2212.11478, Paper]

• F. Neumann, C. Witt (2022): Runtime analysis of the (1+1) EA on weighted sums of transformed linear functions.
  In: Parallel Problem Solving from Nature XVII, PPSN 2022.
  [CoRR abs/2208.05670]

• F. Neumann, C. Witt (2022): Runtime analysis of single- and multi-objective evolutionary algorithms for chance constrained optimization problems with Normally distributed random variables.
  In: International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence, IJCAI-ECAI 2022.
  [Paper, CoRR abs/2109.05799]