Biased random-key genetic algorithms for combinatorial optimization

Submitted to J. of Heuristics, 2009

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.

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