Adaptive genetic algorithms guided by decomposition for PCSPs: application to frequency assignment problems

Abstract

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 generic, meaning that any decompositionmethod 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.

Keywords

optimization problems / partial constraint satisfaction problems / frequency assignment problems / graph decomposition / adaptive genetic algorithm (AGA) / AGA guided by decomposition (AGAGD)

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