https://hal-enac.archives-ouvertes.fr/hal-00937716Barnier, NicolasNicolasBarnierMAIA-OPTIM - ENAC Equipe MAIAA-OPTIM - MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien - ENAC - Ecole Nationale de l'Aviation CivileBrisset, PascalPascalBrissetENAC - Ecole Nationale de l'Aviation CivileOptimization by hybridization of a genetic algorithm with constraint satisfaction techniquesHAL CCSD1998radio linkrobustnessroutingspace explorationsubspace constraintsconstraint optimizationcost functiongenetic algorithmsgenetic mutationsoptimization methods[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Porte, Laurence2014-04-17 16:31:062021-10-19 11:02:562014-04-17 16:33:31enConference papershttps://hal-enac.archives-ouvertes.fr/hal-00937716/document10.1109/ICEC.1998.700115application/pdf1The 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.