Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/123972
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Type: Conference paper
Title: Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem.
Author: Pourhassan, M.
Shi, F.
Neumann, F.
Citation: PPSN. 14th International conference Parallel Problem Solving from Nature - PPSN XIV, 2016 / Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (ed./s), vol.9921, pp.729-739
Publisher: Springer
Publisher Place: Online
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science
ISBN: 978-3-319-45822-9
ISSN: 1063-6560
1530-9304
Conference Name: 14th International conference Parallel Problem Solving from Nature - PPSN XIV (17 Sep 2016 : Edinburgh, UK)
Editor: Handl, J.
Hart, E.
Lewis, P.
López-Ibáñez, M.
Ochoa, G.
Paechter, B.
Statement of
Responsibility: 
Mojgan Pourhassan, Feng Shi and Frank Neumann
Abstract: Evolutionary multiobjective optimization for the classical vertex cover problem has been analysed in Kratsch and Neumann (2013) in the context of parameterized complexity analysis. This article extends the analysis to the weighted vertex cover problem in which integer weights are assigned to the vertices and the goal is to find a vertex cover of minimum weight. Using an alternative mutation operator introduced in Kratsch and Neumann (2013), we provide a fixed parameter evolutionary algorithm with respect to OPT, the cost of an optimal solution for the problem. Moreover, we present a multiobjective evolutionary algorithm with standard mutation operator that keeps the population size in a polynomial order by means of a proper diversity mechanism, and therefore, manages to find a 2-approximation in expected polynomial time. We also introduce a population-based evolutionary algorithm which finds a (1+ɛ)-approximation in expected time O(n·2min{n,2(1-ɛ)OPT}+n3).
Keywords: Parameterized analysis; global SEMO; weighted vertex cover problem.
Rights: © 2019 Massachusetts Institute of Technology
DOI: 10.1162/evco_a_00255
Grant ID: http://purl.org/au-research/grants/arc/DP140103400
http://purl.org/au-research/grants/arc/DP160102401
Published version: https://doi.org/10.1007/978-3-319-45823-6
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Computer Science publications

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