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Paper Abstract and Keywords
Presentation 2025-03-13 12:30
Proposal of SplitCMA-ES for Multimodal Optimization Problems
Tenma Yamada, Kenya Jin'no (TCU) MSS2024-72 NLP2024-113
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes SplitCMA-ES as an improved method of CMA-ES. CMA-ES is known as a powerful black-box optimization method that generates individuals and updates distributions based on a multivariate normal distribution. However, it has been pointed out that there is a risk of getting trapped in local optima in high-dimensional and multimodal problems cite{1}.
Many previous studies that adapt CMA-ES to multimodal problems rely on adjusting the population size. However, in real-world problems, the evaluation cost is high, making it difficult to increase the number of sampled candidate solutions. Therefore, this study proposes a method that divides the search space along the eigencoordinate axes obtained from the eigenvalue decomposition of the covariance matrix and performs independent exploration in each subspace while preserving the correlation among parameters, thereby enhancing adaptability to multimodal problems.
Experimental results show that the proposed method achieves more stable convergence than conventional methods for multimodal functions and effectively reduces the risk of getting trapped in local optima. On the other hand, some cases were observed where the method failed to explore problems with strong variable dependencies.
These findings suggest that the proposed method is a promising search strategy even for real-world problems with high evaluation costs.
Keyword (in Japanese) (See Japanese page) 
(in English) optimization / CMA-ES / SplitCMA-ES / multimodal / evolutionary strategy / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 432, NLP2024-113, pp. 32-35, March 2025.
Paper # NLP2024-113 
Date of Issue 2025-03-06 (MSS, NLP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MSS2024-72 NLP2024-113

Conference Information
Committee NLP MSS  
Conference Date 2025-03-13 - 2025-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyakojima City Central Community Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) MSS, NLP, etc. 
Paper Information
Registration To NLP 
Conference Code 2025-03-NLP-MSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Proposal of SplitCMA-ES for Multimodal Optimization Problems 
Sub Title (in English)  
Keyword(1) optimization  
Keyword(2) CMA-ES  
Keyword(3) SplitCMA-ES  
Keyword(4) multimodal  
Keyword(5) evolutionary strategy  
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1st Author's Name Tenma Yamada  
1st Author's Affiliation Tokyo City University (TCU)
2nd Author's Name Kenya Jin'no  
2nd Author's Affiliation Tokyo City University (TCU)
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Speaker Author-1 
Date Time 2025-03-13 12:30:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2024-72, NLP2024-113 
Volume (vol) vol.124 
Number (no) no.431(MSS), no.432(NLP) 
Page pp.32-35 
#Pages 4 
Date of Issue 2025-03-06 (MSS, NLP) 

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