A Hybrid Parameter Adaptation Based GA and Its Application for Data Clustering

The performance of genetic algorithm (GA) critically depends on the rates of variation operation. In this paper, we propose a hybrid parameter adaptation scheme, which integrates the traditional adaptive and self-adaptive method, to dynamically control the crossover and mutation rate of GA during evolution. Such a scheme can take advantage of both adaptive and self-adaptive mechanisms, thus effectively setting the parameters of GA. The resulting GA has been applied for data clustering. Our results show that the proposed scheme is beneficial and the resulting GA outperforms the adaptive GA or self-adaptive GA for data clustering.