Biased random-key genetic algorithms for combinatorial optimization
José F. Gonçalves(jfgoncalfep.up.pt)
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.
Entry Submitted: 10/09/2009
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