@inproceedings{40c5d91ceab24681b2e436045e71369f,
title = "Parameter Setting of CMA-ES: A Numerical Study on CEC2019 100-Digit Challenge",
abstract = "The evolution strategy with covariance matrix adaptation (CMA-ES) is a well-known algorithm in the family of evolution strategies. It consists of three main mechanisms: derandomized adaptation, cumulative step size, and covariance matrix adaptation. In this paper we aim to improve the CMA- ES and its performance by proper parameter values, better repair mechanism, removal of rank-one update, and a spread- based step size adaptation mechanism. We tested the proposed algorithm by ten test functions in the CEC2019 100-Digit Challenge. The results showed that our algorithm can achieve similar solution quality compared with two recent hybrid adaptive evolutionary algorithms and needs fewer fitness function evaluations.",
keywords = "CMA-ES, adaptive, covariance matrix adaptation, evolution strategy",
author = "Chen, \{Ting Yu\} and Yeh, \{Jia Fong\} and Yeh, \{Tsung Su\} and Chiang, \{Tsung Che\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 ; Conference date: 21-11-2019 Through 23-11-2019",
year = "2019",
month = nov,
doi = "10.1109/TAAI48200.2019.8959900",
language = "English",
series = "Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019",
}