Project "Parameterised analysis of bio-inspired computing - from theory to high performing algorithms"

We develop, from the present simple state of art, an extensive parameterized analysis of bio-inspired computing and transfer these new insights into better performing algorithms. We do this by examining features of important combinatorial optimization problems and their influence on the performance of bio-inspired computing methods. These theoretical investigations are carried out by feature-based analysis and by using parameterized computational complexity analyses of bio-inspired computing methods. These fundamental investigations allow us to get new insights on how different parameters of combinatorial optimization problems influence the runtime behaviour of bio-inspired computing methods. We use the theoretical results obtained to develop new and more effective algorithms for the investigated problems. This is done by developing new bio-inspired computing methods that are particularly target towards the hard instances of a given combinatorial optimization problem. The knowledge gained from our investigations boosts the understanding on how effective bio-inspired computing methods can be design by taking important instance parameters into account. The project has been funded by the Australian Research Council through a Discovery Project 2014-2016.

Publications

Journal Papers

• M. Pourhassan, F. Neumann (2018): Theoretical analysis of local search and simple evolutionary algorithms for the generalized travelling salesperson problem.
  Evolutionary Computation.
  Paper

• S. Nallaperuma, F. Neumann, D. Sudholt (2017): Expected fitness gains of randomized search heuristics for the traveling salesperson problem.
  Evolutionary Computation, Volume 25, Issue 4.
  Paper

• B. Doerr, F. Neumann, A. M. Sutton (2017): Time complexity analysis of evolutionary algorithms on random satisfiable k-CNF formulas
  Algorithmica, Volume 78, Issue 2, 561–586.
  Paper

• D. Corus, P. K. Lehre, F. Neumann, M. Pourhassan (2016): A parameterised complexity analysis of bi-level optimisation with evolutionary algorithms.
  Evolutionary Computation, Volume 14, Issue 1, 183–203.
  Available: [CoRR abs/1401.1905]

• T. Friedrich, F. Neumann (2015): Maximizing submodular functions under Matroid constraints by evolutionary algorithms.
  Evolutionary Computation, Volume 23, Issue 4, 543-558.
  Paper

Conference Papers

• F. Neumann (2017): Parameterized Analysis of Bio-inspired Computing.
  In: IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2017.
  Paper

• 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.
  Available: [CoRR abs/1703.03773]

• E. C. Osuna, W. Gao, D. Sudholt F. Neumann (2017): Speeding up evolutionary multi-objective optimisation through diversity-based parent selection
  In: Genetic and Evolutionary Computation Conference, GECCO 2017, ACM Press.
  Paper

• 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

• M. Pourhassan, F. Shi , F. Neumann (2016): Parameterized analysis of multi-objective evolutionary algorithms and the weighted vertex cover problem.
  In: Parallel Problem Solving from Nature XIV, PPSN 2016.
  Paper

• W. Gao, T. Friedrich, F. Neumann (2016): Fixed-parameter single objective search heuristics for minimum vertex cover.
  In: Parallel Problem Solving from Nature XIV, PPSN 2016.
  Paper

• W. Gao, S. Nallaperuma, F. Neumann (2016): Feature-based diversity optimization for problem instance classification.
  In: Parallel Problem Solving from Nature XIV, PPSN 2016.
  Available: CORR 1510.08568

• B. Doerr, W. Gao, F. Neumann (2016): Runtime analysis of evolutionary diversity maximization for OneMinMax.
  In: Genetic and Evolutionary Computation Conference, GECCO 2016, ACM Press.
  Paper

• T. Friedrich, T. Kötzing, M. S. Krejca, S. Nallaperuma, F. Neumann, M. Schirneck (2016): Fast building block assembly by majority vote crossover.
  In: Genetic and Evolutionary Computation Conference, GECCO 2016, ACM Press.
  Paper

• S. Poursoltan, F. Neumann (2016): Feature-based algorithm selection for constrained continuous optimisation.
  In: IEEE Congress on Evolutionary Computation, IEEE CEC 2016.
  Available: [CoRR abs/1602.02862]

• S. Poursoltan, F. Neumann (2015): A feature-based comparison of evolutionary computing techniques for constrained continuous optimisation.
  In: International Conference on Neural Information Processing, ICONIP 2015.
  Available: [CoRR abs/1509.06842]

• S. Poursoltan, F. Neumann (2015): A feature-based analysis on the impact of set of constraints for ε-constrained differential evolution.
  In: International Conference on Neural Information Processing, ICONIP 2015.
  Available: [CoRR abs/1506.06848]

• F. Neumann, C. Witt (2015): On the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling.
  In: International Joint Conference on Artificial Intelligence, IJCAI 2015.
  Available: [CoRR abs/1504.06363]

• M. Pourhassan, W. Gao, F. Neumann (2015): Maintaining 2-approximations for the dynamic vertex cover Problem using evolutionary algorithms.
  In: Genetic and Evolutionary Computation Conference, GECCO 2015, ACM Press.
  Paper

• M. Pourhassan, F. Neumann (2015): On the impact of local search operators and variable neighbourhood search for the generalized travelling salesperson problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2015, ACM Press.
  Paper

• Doerr, F. Neumann, A. M. Sutton (2015): Improved runtime bounds for the (1+1) EA on random 3-CNF formulas based on fitness-distance correlation.
  In: Genetic and Evolutionary Computation Conference, GECCO 2015, ACM Press. (Best Paper Award in the track "Theory")
  Paper

• T. Friedrich, F. Neumann (2014): Maximizing submodular functions under matroid constraints by multi-objective evolutionary algorithms.
  In: Parallel Problem Solving from Nature XIII, PPSN 2014. (Nominated for Best Paper Award)
  Paper

• A. M. Sutton, F. Neumann (2014): Average-case analysis of evolutionary algorithms on high-density satisfiable 3-CNF formulas.
  In: Parallel Problem Solving from Nature XIII, PPSN 2014.
  Paper

• S. Nallaperuma, M. Wagner, F. Neumann (2014): Parameter prediction based on features of evolved instances for ant colony optimization and the traveling salesperson problem.
  In: Parallel Problem Solving from Nature XIII, PPSN 2014.
  Paper

• W. Gao, F. Neumann (2014): Runtime analysis for maximizing population diversity in single-objective optimization.
  In: Genetic and Evolutionary Computation Conference, GECCO 2014, ACM Press.
  Paper

• S. Nallaperuma, F. Neumann, D. Sudholt (2014): A fixed budget analysis of randomized search heuristics for the traveling salesperson problem.
  In: Genetic and Evolutionary Computation Conference, GECCO 2014, ACM Press. (Nominated for Best Paper Award in the track "Genetic Algorithms")
  Paper

• S. Poursoltan, F. Neumann (2014): A feature-based analysis on the impact of linear constraints for e-constrained differential evolution.
  In: IEEE Congress on Evolutionary Computation, IEEE CEC 2014, IEEE Press.
  Paper