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Biased random-key genetic algorithms for combinatorial optimization

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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.

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Authors and Affiliations

  1. LIAAD, Faculdade de Economia do Porto, Universidade do Porto, Porto, Portugal

    José Fernando Gonçalves

  2. Algorithms & Optimization Research Department, AT&T Labs Research, Florham Park, NJ, USA

    Mauricio G. C. Resende

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  1. José Fernando Gonçalves
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  2. Mauricio G. C. Resende
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Correspondence to Mauricio G. C. Resende.

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This research was partially supported by Fundação para a Ciência e Tecnologia (FCT) project PTDC/GES/72244/2006. AT&T Labs Research Technical Report.

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Gonçalves, J.F., Resende, M.G.C. Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17, 487–525 (2011). https://doi.org/10.1007/s10732-010-9143-1

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