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Home Metaheuristics articles Recent Publications Journal of Heuristics 17(5), 2011

Journal of Heuristics 17(5), 2011

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Biased random-key genetic algorithms for combinatorial optimization

by José Fernando Gonçalves and Mauricio G. C. Resende

Abstract

Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154–160, 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

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@article {springerlink:10.1007/s10732-010-9143-1,
   author = {Gonçalves, José and Resende, Mauricio},
   title = {Biased random-key genetic algorithms for combinatorial optimization},
   journal = {Journal of Heuristics},
   pages = {487-525},
   volume = {17},
   issue = {5},
   url = {http://dx.doi.org/10.1007/s10732-010-9143-1},
   year = {2011}
}

 

Last Updated on Monday, 26 September 2011 05:59  

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