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

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 C...

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Bibliographic Details
Published in:The Journal of the Operational Research Society 2005-10, Vol.56 (10), p.1213-1223
Main Authors: Vasko, F J, Knolle, P J, Spiegel, D S
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Chromosomes
Confidence interval
Degrees of freedom
Exact sciences and technology
Flows in networks. Combinatorial problems
Genes
Genetic algorithms
Genetic mutation
Heuristic
Heuristics
hybrid heuristics
Hybridity
Mutation
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Population
set covering problem
Studies
Theoretical Papers
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Summary: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.
ISSN:0160-5682
1476-9360
DOI:10.1057/palgrave.jors.2601919