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Biased random-key genetic algorithms for combinatorial optimization

José F. Gonçalves(jfgoncal***at***fep.up.pt)
Mauricio G.C. Resende(mgcr***at***research.att.com)

Abstract: Random-key genetic algorithms were introduced by Bean (1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. Biased random-key genetic algorithms are a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. After introducing the basics of biased random-key genetic algorithms, the paper discusses in some detail implementation issues, illustrating the ease in which sequential and parallel heuristics based on biased random-key genetic algorithms can be developed. A survey of applications that have recently appeared in the literature is also given.

Keywords: Genetic algorithms, biased random-key genetic algorithms, random-key genetic algorithms, combinatorial optimization, metaheuristics.

Category 1: Combinatorial Optimization (Meta Heuristics )

Category 2: Global Optimization (Stochastic Approaches )

Citation: AT&T Labs Research Technical Report, Florham Park, NJ, October 9, 2009.

Download: [PDF]

Entry Submitted: 10/09/2009
Entry Accepted: 10/13/2009
Entry Last Modified: 10/09/2009

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