Abstract
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. © 2014 Springer International Publishing Switzerland.
Cite
CITATION STYLE
Filipiak, P., & Lipinski, P. (2014). Univariate marginal distribution algorithm with markov chain predictor in continuous dynamic environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8669 LNCS, pp. 404–411). Springer Verlag. https://doi.org/10.1007/978-3-319-10840-7_49
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
