Bossek, J., Neumann, F., Peng, P. et al. (1 more author) (2020) More effective randomized search heuristics for graph coloring through dynamic optimization. In: GECCO 2020: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2020 : Genetic and Evolutionary Computation Conference, 08-12 Jul 2020, Cancún, Mexico. ACM Digital Library , pp. 1277-1285. ISBN 9781450371285
Abstract
Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + λ) RLS) lead to an exponential speedup in λ. Finally, an island model using 3 islands succeeds in an optimal time of Θ(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is an author-produced version of a paper subsequently published in GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Evolutionary algorithms; dynamic optimization; running time analysis; theory |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Apr 2020 10:47 |
Last Modified: | 15 Oct 2020 11:37 |
Status: | Published |
Publisher: | ACM Digital Library |
Refereed: | Yes |
Identification Number: | 10.1145/3377930.3390174 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159918 |