Combine & conquer : genetic algorithm and CP for optimization

Nicolas Barnier (1) , Pascal Brisset (2)

1 MAIA-OPTIM - ENAC Equipe MAIAA-OPTIM
2 ENAC - Ecole Nationale de l'Aviation Civile

We 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.

Mots clés: genetic algorithms; hybridization; optimization

Domaines: Optimisation et contrôle [math.OC]

Nicolas Barnier, Pascal Brisset. Combine & conquer : genetic algorithm and CP for optimization. CP 1998, 4th Conference on Principles and Practice of Constraint Programming, Oct 1998, Pisa, Italy. 1520, pp 463-477, 1998, ⟨10.1007/3-540-49481-2_34⟩. ⟨hal-00937732⟩