A parallel genetic algorithm for the graph partitioning problem
Résumé
Genetic algorithms are stochastic search and optimization techniques which can be used for a wide range of applications. This paper addresses the application of genetic algorithms to the graph partitioning problem. Standard genetic algorithms with large populations suffer from lack of efficiency (quite high execution time). A massively parallel genetic algorithm is proposed, an implementation on a SuperNode® of Transputers® and results of various benchmarks are given. The parallel algorithm shows a superlinear speed-up, in the sense that when multiplying the number of processors by p, the time spent to reach a solution with a given score, is divided by kp (k>1). A comparative analysis of our approach with hill-climbing algorithms and simulated annealing is also presented. The experimental measures show that our algorithm gives better results concerning both the quality of the solution and the time needed to reach it.