This paper proposes Adaptive Genetic Algorithms Guided by structural knowledges coming from decomposition methods, for solving PCSPs. The family of algorithms called AGAGD_x_y is designed to be doubly genetic, meaning...This paper proposes Adaptive Genetic Algorithms Guided by structural knowledges coming from decomposition methods, for solving PCSPs. The family of algorithms called AGAGD_x_y is designed to be doubly genetic, meaning that any decomposition method and different heuristics for the genetic operators can be considered. To validate the approach, the decomposition algorithm due to Newman was used and several crossover operators based on structural knowledge such as the cluster, separator and the cut were tested. The experimental results obtained on the most challenging Minimum Interference-FAP problems of CALMA instances are very promising and lead to interesting perspectives to be explored in the future.展开更多
基金supported by National Nature Science Foundation for Excellent Innovation Research Group of China (71021061)National Nature Science Foundation of China (70701008, 70871021, 90924016 and71171043)Humanities and Society Science Plan Foundation of Ministry of Education of China (11YJA630180)
文摘This paper proposes Adaptive Genetic Algorithms Guided by structural knowledges coming from decomposition methods, for solving PCSPs. The family of algorithms called AGAGD_x_y is designed to be doubly genetic, meaning that any decomposition method and different heuristics for the genetic operators can be considered. To validate the approach, the decomposition algorithm due to Newman was used and several crossover operators based on structural knowledge such as the cluster, separator and the cut were tested. The experimental results obtained on the most challenging Minimum Interference-FAP problems of CALMA instances are very promising and lead to interesting perspectives to be explored in the future.
