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Journal of Heuristics

, Volume 17, Issue 5, pp 487–525 | Cite as

Biased random-key genetic algorithms for combinatorial optimization

  • José Fernando Gonçalves
  • Mauricio G. C. Resende
Article

Abstract

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.

Keywords

Genetic algorithms Biased random-key genetic algorithms Random-key genetic algorithms Combinatorial optimization Metaheuristics 

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • José Fernando Gonçalves
    • 1
  • Mauricio G. C. Resende
    • 2
  1. 1.LIAAD, Faculdade de Economia do PortoUniversidade do PortoPortoPortugal
  2. 2.Algorithms & Optimization Research DepartmentAT&T Labs ResearchFlorham ParkUSA

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