文章基本信息

标题：Proposal and Evaluation of Functionally Specialized CMA-ES
本地全文：下载
作者：Youhei Akimoto ; Jun Sakuma ; Isao Ono 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2009
卷号：24
期号：1
页码：58-68
DOI：10.1527/tjsai.24.58
出版社：The Japanese Society for Artificial Intelligence
摘要：This paper aims the design of efficient and effective optimization algorithms for function optimization. For that purpose we present a new framework of the derandomized evolution strategy with covariance matrix adaptation, which combines the hybrid step size adaptation that is proposed in this paper as a robust alternate to the cumulative step size adaptation and normalization mechanism of covariance matrix. Experiment is conducted on 8 classical unimodal and multimodal test functions and the performance of the proposed strategy is compared with that of the standard strategy. Results show that the proposed strategy beats the standard strategy when the population size becomes larger than the default one, while the performance of proposed strategy is as well or better than that of the standard strategy under the default population size.
关键词：evolution strategy ; covariance matrix adaptation ; step size adaptation ; function specialization