Biased random‐key genetic algorithms for combinatorial optimization

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.