Bossek, J., Neumann, F., Peng, P. orcid.org/0000-0003-2700-5699 et al. (1 more author) (2019) Runtime analysis of randomized search heuristics for dynamic graph coloring. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 19). Genetic and Evolutionary Computation Conference (GECCO '19), 13-17 Jul 2019, Prague, Czech Republic. ACM , pp. 1443-1451. ISBN 9781450361118
Abstract
We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors.
We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 ACM. [https://dl.acm.org/] This is an author-produced version of a paper accepted for publication in the Proceedings of GECCO 2019. 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: | 01 May 2019 11:28 |
Last Modified: | 09 Oct 2019 13:47 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3321707.3321792 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145579 |