An empirical study of hybrid genetic algorithms for the set covering problem

F J Vasko (), P J Knolle and D S Spiegel
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F J Vasko: Kutztown University
P J Knolle: Tulane University
D S Spiegel: Kutztown University

Journal of the Operational Research Society, 2005, vol. 56, issue 10, 1213-1223

Abstract: Abstract The purpose of this paper is to explore the computational performance of several hybrid algorithms that are extensions of a basic genetic algorithm (GA) approach for solving the set covering problem (SCP). We start by making several enhancements to a GA for the SCP that was proposed by Beasley and Chu. Next, several hybrid solution approaches are introduced that combine the GA with various local neighbourhood search approaches, with a form of the greedy randomized adaptive search procedure, and with an estimation of distribution algorithms approach. Using Beasley's library of SCPs extensive computational results are generated for the hybrid solution approaches defined in this paper. Statistical analyses of the results are performed.

Keywords: set covering problem; genetic algorithms; hybrid heuristics (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (3)

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DOI: 10.1057/palgrave.jors.2601919

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