Clustering data using a modified integer genetic algorithm (IGA)

This paper developed a modified genetic algorithm with integer representation (IGA) for cluster analysis problem. The IGA method expands the basic concepts of conventional GAs to include fitness scaling, a modified selection operator, and three newly proposed genetic operators, i.e., competition, self-reproduction and diversification. Moreover, a new clustering criterion was introduced and compared with the commonly used square-error criterion. Clustering of simulated and real chemical data showed that IGA consistently outperformed conventional GAs both in search efficiency and in search precision, and the introduced criterion provided better performance than the square-error criterion.