Clustering Using Pipelined Genetic Algorithm

In this paper, an attempt is made to address the clustering problem using a pipelined genetic algorithm, which is a faster version of the conventional genetic algorithm as the underlying search and optimization tool. In pipelined Genetic Algorithm, PLGA, the operations of conventional genetic algorithms are pipelined in such a way that their execution overlaps in time; thereby providing an overall speedup of the process. Since conventional selection methods require computation of fitness values of all the chromosomes to be completed before selection, in PLGA, the SA-selection is employed. SA-selection uses a statistical distribution function similar to one used in simulated annealing. With this selection method, operations of GA can be easily pipelined. Here, both crisp and fuzzy clustering problems are used for comparing the performance of PLGA and conventional GA. An analysis of the speedup obtained in case of the PLGA is provided. Hardware configuration required for clustering is presented.

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