Illinois Genetic Algorithms Laboratory

Pelikan, M., Goldberg, D.E. (2000)
TR No.: 2000013 | Download PDF | Download PS

Abstract:

This paper introduces clustering as a tool to improve the effects of recombination and incorporate niching in evolutionary algorithms. Instead of processing the entire set of parent solutions, the set is first clustered and the solutions in each of the clusters are processed separately. This alleviates the problem of symmetry which is often a major difficulty of many evolutionary algorithms in combinatorial optimization. Furthermore, it incorporates niching into genetic algorithms and, for the firsttime, the probabilistic model-building genetic algorithms. The dynamics and performance of the proposed method are illustrated on example problems.