Project "Bioinspired Computing for Problems with Dynamically Changing Constraints"
The aim project is designs bioinspired computing methods for dynamically changing environments and build up a theory to support the design of such methods. Dynamic problems appear frequently in realworld applications such as logistics for mining and are usually subject to a large set of constraints. These constraints change over time due to changes in resources and having algorithms that can deal with such dynamic changes delivers direct benefit to decision makers. The project provides a foundational basis for the design for bio inspired algorithms dealing with dynamically changing constraints and provide approaches for dealing with such important industrial problems. The work carried out has been supported by the Australian Research Council through a Discovery Project.Selected Key Publications

V. Roostapour, A. Neumann, F. Neumann (2022): Single and multiobjective evolutionary algorithms for the knapsack problem with dynamically changing constraints.
Theoretical Computer Science.
[CoRR abs/2004.12574] 
V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2022): Pareto optimization for subset selection with dynamic cost constraints.
Artificial Intelligence
Available: [CoRR abs/1811.07806] 
F. Shi, F. Neumann, J. Wang (2020): Runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem.
Algorithmica.
[CoRR abs/2001.08903] 
M. Pourhassan, F. Shi , F. Neumann (2019): Parameterized analysis of multiobjective evolutionary algorithms and the weighted vertex cover problem.
Evolutionary Computation, Vol. 27, No. 4, 559–575
Paper 
M. Pourhassan, V. Roostapour, F. Neumann, (2019): Runtime analysis of RLS and (1+1) EA for the dynamic weighted vertex cover problem.
Theoretical Computer Science.
Available: [CoRR abs/1903.02195] 
F. Shi, M. Schirneck, T. Friedrich, T. Kötzing, F. Neumann (2019): Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints.
Algorithmica, Volume 81, 828–857
[Paper] 
M. R. Przybylek, A. Wierzbicki, M. Michalewicz (2018): Decomposition algorithms for a multihard problem.
Evolutionary Computation.
Paper 
T. Friedrich, T. Kötzing, J. A. G. Lagodzinski, F. Neumann, M. Schirneck (2018): Analysis of the (1+1) EA on subclasses of linear functions under uniform and linear constraints.
Theoretical Computer Science.
Paper 
A. V. Do, F. Neumann (2021): Pareto optimization for subset selection with dynamic partition matroid constraints.
In: ThirtyFifth AAAI Conference on Artificial Intelligence, AAAI 2021.
[CoRR abs/2012.08738] 
M. HasaniShoreh, R, Hermoza Aragones, F. Neumann (2020): Using neural networks and diversifying differential evolution for dynamic optimisation.
In: IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2020.
[CoRR abs/2008.04002] 
R. Sachdeva, F. Neumann, M. Wagner (2020): The dynamic travelling thief problem: benchmarks and performance of evolutionary algorithms.
In: International Conference on Neural Information Processing, ICONIP 2020.
[CoRR abs/2004.12045] 
A. V. Do, F. Neumann (2020): Maximizing submodular or monotone functions under partition matroid constraints by multiobjective evolutionary algorithms.
In: Parallel Problem Solving from Nature XVI, PPSN 2020.
[CoRR abs/2006.12773] J. Bossek, F. Neumann, P. Peng, D. Sudholt (2020): More effective randomized search heuristics for graph coloring through dynamic optimisation.
In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
[CoRR abs/2005.13825]
M. HasaniShoreh, R. Hermoza Aragonés, F. Neumann (2020): Neural networks in evolutionary dynamic constrained optimization: computational cost and benefits.
In: 24th European Conference on Artificial Intelligence, ECAI 2020.
[CoRR abs/2001.11588] 
V. Doskoc, T. Friedrich, A. Göbel, A. Neumann, F. Neumann, F. Quinzan (2020): NonMonotone submodular maximization with multiple knapsacks in static and dynamic settings.
In: 24th European Conference on Artificial Intelligence, ECAI 2020.
Paper [CoRR abs/1911.06791] 
B. Doerr, C. Doerr, A. Neumann, F. Neumann, A. M. Sutton (2020): Optimization of chanceconstrained submodular functions.
In: ThirtyFourth AAAI Conference on Artificial Intelligence, AAAI 2020.
[CoRR abs/1911.11451] 
M. HasaniShoreh, F. Neumann: (2019): On the use of diversity mechanisms in dynamic constrained continuous optimization.
In: International Conference on Neural Information Processing, ICONIP 2019.
[CoRR abs/1910.06062] 
F. Neumann, M. Pourhassan, C. Witt: (2019): Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Paper 
B. Doerr, C. Doerr, F. Neumann: (2019): Fast reoptimization via structural diversity.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Available: [CoRR abs/1902.00304] 
J. Bossek, F. Neumann, P. Peng, D. Sudholt: (2019): Runtime analysis of randomized search heuristics for dynamic graph coloring.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Available: [Whiterose145579] 
M. H. Shoreh, M.Y. AmecaAlducin, W. Blaikie, F. Neumann, M. Schoenauer (2019): On the behaviour of differential evolution for problems with dynamic linear constraints.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2019.
Available: [CoRR abs/1905.04099] 
V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2019): Pareto optimization for subset selection with dynamic cost constraints.
In: ThirtyThird AAAI Conference on Artificial Intelligence, AAAI 2019.
Available: [CoRR abs/1811.07806], AAAI version 
T. Friedrich, A. Göbel, F. Neumann, F. Quinzan, R. Rothenberger (2019): Greedy maximization of functions with bounded curvature under partition matroid constraints.
In: ThirtyThird AAAI Conference on Artificial Intelligence, AAAI 2019.
Available: [CoRR abs/1811.05351], AAAI version 
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 
F. Shi, F. Neumann, J. Wang (2018): Runtime analysis of randomized search heuristics for the dynamic weighted vertex cover problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2018, ACM Press.
Paper 
M. Y. AmecaAlducin, M. HasaniShoreh, W. Blaikie, F. Neumann, E. Mezura Montes (2018): A comparison of constraint handling techniques for dynamic constrained optimization problems.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2018.
Available: [CoRR abs/1802.05825] 
M. Y. AmecaAlducin, M. HasaniShoreh, F. Neumann (2018): On the use of repair methods in differential evolution for dynamic constrained optimization.
In: International Conference on the Applications of Evolutionary Computation, EVOAPPLICATIONS 2018.
Paper 
M. Pourhassan, V. Roostapour, F. Neumann (2017): Improved runtime analysis of RLS and (1+1) EA for the dynamic vertex cover problem.
In: IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2017.
Available extended version: [CoRR abs/1903.02195] 
F. Shi, M. Schirneck, T. Friedrich, T. Kötzing, F. Neumann (2017): Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints
In: Genetic and Evolutionary Computation Conference, GECCO 2017, ACM Press.
Paper 
T. Friedrich, T. Kötzing, J. A. G. Lagodzinski, F. Neumann, M. Schirneck (2017): Analysis of the (1+1) EA on subclasses of linear functions under uniform and linear constraints.
In: Foundations of Genetic Algorithms XIV, FOGA 2017, ACM Press.
Paper 
M. Pourhassan, T. Friedrich, F. Neumann (2017): On the use of the dual formulation for minimum vertex cover in evolutionary algorithms.
In: Foundations of Genetic Algorithms XIV, FOGA 2017, ACM Press.
Paper 
M. Pourhassan, F. Shi , F. Neumann (2016): Parameterized analysis of multiobjective evolutionary algorithms and the weighted vertex cover problem.
In: Parallel Problem Solving from Nature XIV, PPSN 2016.
Paper