J. of Heuristics, vol. 17, pp. 487-525, 2011, DOI: 10.1007/s10732-010-9143-1
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.
PDF file of full paper
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