A robust evolutionary algorithm for global optimization

This paper studies an evolutionary algorithm for global optimization. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing-based mutations and self-adaptive mutations. The proposed approach is experimentally analyzed...

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Bibliographic Details
Published inEngineering optimization Vol. 34; no. 5; pp. 405 - 425
Main Authors Yang, Jinn-Moon, Lin, Chin-Jen, Kao, Cheng-Yan
Format Journal Article
LanguageEnglish
Published Taylor & Francis Group 01.01.2002
Subjects
Adaptive Rules
Evolutionary Algorithms
Family Competition
Global Optimization
Multiple Mutation Operators
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Summary:This paper studies an evolutionary algorithm for global optimization. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing-based mutations and self-adaptive mutations. The proposed approach is experimentally analyzed by showing that its components can integrate with one another and possess good local and global properties. Following the description of implementation details, the approach is then applied to several widely used test sets, including problems from international contests on evolutionary optimization. Numerical results indicate that the new approach performs very robustly and is competitive with other well-known evolutionary algorithms.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0305-215X
1029-0273
DOI:10.1080/03052150214019