Journal
JOURNAL OF HEURISTICS
Volume 17, Issue 5, Pages 487-525Publisher
SPRINGER
DOI: 10.1007/s10732-010-9143-1
Keywords
Genetic algorithms; Biased random-key genetic algorithms; Random-key genetic algorithms; Combinatorial optimization; Metaheuristics
Funding
- Fundacao para a Ciencia e Tecnologia (FCT) [PTDC/GES/72244/2006]
- Fundação para a Ciência e a Tecnologia [PTDC/GES/72244/2006] Funding Source: FCT
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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.
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