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An Improved CMA-ES for Solving Large Scale Optimization Problem
In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous genera...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354820/ http://dx.doi.org/10.1007/978-3-030-53956-6_34 |
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author | Jin, Jin Yang, Chuan Zhang, Yi |
author_facet | Jin, Jin Yang, Chuan Zhang, Yi |
author_sort | Jin, Jin |
collection | PubMed |
description | In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous generation of individuals to evaluate the parameters of the distribution of the next generation. These estimates can be considered as approximate gradient information, which complete covariance information is not required. Thus GI-ES is friendly to large scale optimization problems. Comparative experiments have been done on state-of-the-art algorithms. The results proved the effectiveness and efficiency of GI-ES for large scale optimization problems. |
format | Online Article Text |
id | pubmed-7354820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73548202020-07-13 An Improved CMA-ES for Solving Large Scale Optimization Problem Jin, Jin Yang, Chuan Zhang, Yi Advances in Swarm Intelligence Article In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous generation of individuals to evaluate the parameters of the distribution of the next generation. These estimates can be considered as approximate gradient information, which complete covariance information is not required. Thus GI-ES is friendly to large scale optimization problems. Comparative experiments have been done on state-of-the-art algorithms. The results proved the effectiveness and efficiency of GI-ES for large scale optimization problems. 2020-06-22 /pmc/articles/PMC7354820/ http://dx.doi.org/10.1007/978-3-030-53956-6_34 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Jin, Jin Yang, Chuan Zhang, Yi An Improved CMA-ES for Solving Large Scale Optimization Problem |
title | An Improved CMA-ES for Solving Large Scale Optimization Problem |
title_full | An Improved CMA-ES for Solving Large Scale Optimization Problem |
title_fullStr | An Improved CMA-ES for Solving Large Scale Optimization Problem |
title_full_unstemmed | An Improved CMA-ES for Solving Large Scale Optimization Problem |
title_short | An Improved CMA-ES for Solving Large Scale Optimization Problem |
title_sort | improved cma-es for solving large scale optimization problem |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354820/ http://dx.doi.org/10.1007/978-3-030-53956-6_34 |
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