Conference papers
Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques
Abstract : The authors introduce a new optimization method based on a genetic algorithm (GA) mixed with constraint satisfaction problem (CSP) techniques. The approach is designed for combinatorial problems whose search spaces are too large and/or objective functions too complex for usual CSP techniques and whose constraints are too complex for conventional genetic algorithm. The main idea is the handling of sub-domains of the CSP variables by the genetic algorithm. The population of the genetic algorithm is made up of strings of sub-domains whose fitness are computed through the resolution of the corresponding ?sub-CSPs? which are somehow much easier than the original problem. They provide basic and dedicated recombination and mutation operators with various degrees of robustness. The first set of experimentations adresses a naive formulation of the vehicle routing problem (VRP) and the radio link frequency assignment problem (RLFAP). The results are quite encouraging as one outperforms CSP techniques and genetic algorithm alone.
Cited literature [7 references]
https://hal-enac.archives-ouvertes.fr/hal-00937716
Contributor : Laurence Porte <>
Submitted on : Thursday, April 17, 2014 - 4:31:06 PM
Last modification on : Wednesday, July 24, 2019 - 11:48:02 PM
Document(s) archivé(s) le : Sunday, April 9, 2017 - 1:50:12 AM
Contributor : Laurence Porte <>
Submitted on : Thursday, April 17, 2014 - 4:31:06 PM
Last modification on : Wednesday, July 24, 2019 - 11:48:02 PM
Document(s) archivé(s) le : Sunday, April 9, 2017 - 1:50:12 AM
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