Biased random-key genetic algorithms for combinatorial optimization

• Published in: Journal of heuristics Vol. 17; no. 5; pp. 487 - 525
• Main Authors: Gonçalves, José Fernando, Resende, Mauricio G. C.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.10.2011; Springer Science + Business Media; Springer Nature B.V
• Subjects: Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Chromosomes; Genetic algorithms; Heuristic; Inventory control; Management Science; Mathematical models; Mathematics; Mathematics and Statistics; Mutation; Operations Research; Operations Research/Decision Theory; Optimization; Optimization algorithms; Population; Production planning; Studies; Traveling salesman problem; Combinatorial optimization; Metaheuristics; Biased random-key genetic algorithms; Random-key genetic algorithms; Genetic algorithms
• ISSN: 1381-1231; 1572-9397
• DOI: 10.1007/s10732-010-9143-1

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.