https://enac.hal.science/hal-00937732Barnier, 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 CivileCombine & conquer : genetic algorithm and CP for optimizationHAL CCSD1998genetic algorithmshybridizationoptimization[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Porte, Laurence2014-04-17 16:27:062021-10-19 11:02:562014-04-17 16:33:31enConference posterhttps://enac.hal.science/hal-00937732/document10.1007/3-540-49481-2_34application/pdf1We introduce a new optimization method based on a Genetic Algorithm (GA) combined 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 adaptation are computed through the resolution of the corresponding ''sub-CSPs'' which are somehow much easier than the original problem. We provide basic and dedicated recombination and mutation operators with various degrees of robustness. The first set of experimentations adresses a naïve formulation of a Vehicle Routing Problem (VRP). The results are quite encouraging as we outperform CSP techniques and genetic algorithm alone on these formulations.