Chaos elitism estimation of distribution algorithm

Abstract

Estimation of distribution algorithm (EDA) is a kind of EAs, which is based on the technique of probabilistic model and sampling. This paper presents a chaos elitism EDA to improve the performance of traditional EDA to solve high dimensional optimization problems. The famous elitism strategy is introduced to maintain a good convergent performance. The chaos perturbation strategy is used to improve the local search ability. Some simulation experiments conducted to verify the performance of CEEDA. The results of CEEDA are promising, and it is comparable with other EDA.