Project "Evolutionary diversity optimisation"

This project aims to build up and establish the area of evolutionary diversity optimisation. The project will cover the design and application of evolutionary diversity optimisation methods to complex problems of significance and high economic benefit and build up the theoretical foundations of these methods. The project is expected benefit decision makers by providing them a diverse set of high quality alternatives to choose from. This project will allow them to make highly informed decisions and lead to more reliable solutions for optimisation problems, in areas of high economic impact such as manufacturing and supply chain management. The work carried out has been supported by the Australian Research Council through a Discovery Project.

Selected Key Publications

Journal Papers

• A. V. Do, M. Guo, A. Neumann, F. Neumann (2022): Analysis of evolutionary diversity optimisation for permutation problems.
  ACM Transactions on Evolutionary Learning and Optimization.
  [CoRR abs/2102.11469]

• W. Gao, S. Nallaperuma, F. Neumann (2021): Feature-based Diversity Optimization for Problem Instance Classification.
  Evolutionary Computation.
  Available: [CORR 1510.08568, Paper]

Conference Papers

• A. Nikfarjam, R. Rothenberger, F. Neumann, T. Friedrich: (2023): Evolutionary diversity optimisation in constructing satisfying assignments.
  In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
  [CoRR abs/2305.11457]

• A. Nikfarjam, A. V. Do, F. Neumann (2022): Analysis of quality diversity algorithms for the knapsack problem.
  In: Parallel Problem Solving from Nature XVII, PPSN 2022.
  [CoRR abs/2207.14037]

• A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann (2022): Co-evolutionary diversity optimisation for the traveling thief problem.
  In: Parallel Problem Solving from Nature XVII, PPSN 2022.
  [CoRR abs/2207.14036]

• A. Nikfarjam, A. Moosavi, A. Neumann, F. Neumann (2022): Computing high-quality solutions for the patient admission scheduling problem using evolutionary diversity optimisation.
  In: Parallel Problem Solving from Nature XVII, PPSN 2022.
  [CoRR abs/2207.14112]

• A. Neumann, D. Antipov, F. Neumann (2022): Coevolutionary Pareto diversity optimization.
  In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
  [CoRR abs/2204.05457]

• D. Goel, M. H. Ward-Graham, A. Neumann, F. Neumann, H. Nguyen, M. Guo (2022): Defending active directory by combining neural network based dynamic program and evolutionary diversity optimisation.
  In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
  [CoRR abs/2204.03397 ]

• A. Nikfarjam, A. Neumann, F. Neumann (2022): Evolutionary diversity optimisation for the traveling thief problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
  [CoRR abs/2204.02709]

• V. A. Do, M. Guo, A. Neumann, F. Neumann (2022): Niching-based evolutionary diversity optimization for the traveling salesperson problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
  [CoRR abs/2201.10316]

• A. Nikfarjam, A. Neumann, F. Neumann (2022): On the use of quality diversity algorithms for the traveling thief problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press. (Nominated for Best Paper Award in the track "Evolutionary Combinatorial Optimization and Metaheuristics")
  [CoRR abs/2112.08627]

• J. Bossek, F. Neumann (2022): Exploring the feature space of TSP instances using quality diversity.
  In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
  [CoRR abs/2202.02077]

• 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]

• 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]

• 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]

• 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]

• J. Bossek, F. Neumann (2021): Evolutionary diversity optimization and the minimum spanning tree problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
  [CoRR abs/2010.10913]

• 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]

• 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

• B. Doerr, C. Doerr, F. Neumann: (2019): Fast re-optimization via structural diversity.
  In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
  Available: [CoRR abs/1902.00304]

• 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")
  Available: [CoRR abs/1811.06804, Paper]