Niche Gene Expression Programming Based on Clustering Model

Abstract

A hybrid niching gene expression programming algorithm, which combines the niching method and clustering model, is proposed. Similar to other evolution algorithms, GEP also has the problem of premature convergence. Niching method is critical to keep diversity among the population and to use this diversity as resource for exploratory evolution. K-means clustering algorithm was used to cluster the near individuals and to build the niche. This model can make GEP jump out of the local optimization at a greater probability and find the global optimization. Experimental results on function finding problems show that the algorithm has higher precision and better search ability than the basic GEP.