Univariate Marginal Distribution Algorithm with Markov Chain Predictor in Continuous Dynamic Environments

摘要

This paper presents an extension of the continuous Univariate Marginal Distribution Algorithm with the prediction mechanism based on a Markov chain model in order to improve the reactivity of the algorithm in continuous dynamic optimization problems. Also a population diversification into exploring, exploiting and anticipating fractions is proposed with the auto-adaptation mechanism for updating dynamically the sizes of these fractions. The proposed approach is tested on the popular benchmark functions with the recurring type of changes.