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Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques

Nicolas Barnier 1 Pascal Brisset 2
MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
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.
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Nicolas Barnier, Pascal Brisset. Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques. IEEE 1998, World Congress on Computational Intelligence, May 1998, Anchorage, United States. pp 645 - 649, ⟨10.1109/ICEC.1998.700115⟩. ⟨hal-00937716⟩



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