Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/128925
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Type: | Conference paper |
Title: | Runtime analysis of randomized search heuristics for the dynamic weighted vertex cover problem |
Author: | Shi, F. Neumann, F. Wang, J. |
Citation: | Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO'18), 2018 / Aguirre, H.E., Takadama, K. (ed./s), pp.1515-1522 |
Publisher: | Association for Computing Machinery |
Publisher Place: | New York, NY |
Issue Date: | 2018 |
ISBN: | 9781450356183 |
Conference Name: | Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2018 - 19 Jul 2018 : Kyoto, Japan) |
Editor: | Aguirre, H.E. Takadama, K. |
Statement of Responsibility: | Feng Shi, Frank Neumann, Jianxin Wang |
Abstract: | Randomized search heuristics such as evolutionary algorithms are frequently applied to dynamic combinatorial optimization problems. Within this paper, we present a dynamic model of the classic Weighted Vertex Cover problem and analyze the performances of the two well-studied algorithms Randomized Local Search and (1+1) EA adapted to it, to contribute to the theoretical understanding of evolutionary computing for problems with dynamic changes. In our investigations, we use an edge-based representation based on the dual formulation of the problem and study the expected runtimes that the two algorithms require to maintain a 2-approximate solution when the given weighted graph is modified by an edgeediting or weight-editing operation. Considering the weights on the vertices may be exponentially large with respect to the size of the graph, the step size adaption strategy is incorporated. Our results show that both algorithms can recompute 2-approximate solutions for the studied dynamic changes efficiently. |
Keywords: | runtime analysis; dynamic weighted vertex cover problem; graphediting operation; evolutionary algorithm; randomized local search |
Rights: | © 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery. |
DOI: | 10.1145/3205455.3205580 |
Grant ID: | http://purl.org/au-research/grants/arc/DP160102401 |
Published version: | https://dl.acm.org/doi/proceedings/10.1145/3205455 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_128925.pdf | Accepted version | 799.71 kB | Adobe PDF | View/Open |
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