A Parallel Genetic Algorithm for Clustering
نویسندگان
چکیده
Parallelization of genetic algorithms (GAs) has received considerable attention in recent years. The reason for this is the availability of suitable computational resources and the need for solving harder problems in reasonable time. We describe a new parallel self-adaptive GA for solving the data clustering problem. The algorithm utilizes island parallelization implemented using genebank model, in which GA processes communicate with each other only through the genebank process. This model allows one to easily implement different migration topologies. Experiments show that significant speedup can be reached by parallelization. The effect of migration parameters is also studied and the development of diversity is examined by several measures, some of which are new.
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