A genetic algorithm with memory for mixed discrete–continuous design optimization

This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the...

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Bibliographic Details
Published inComputers & structures Vol. 81; no. 20; pp. 2003 - 2009
Main Authors Gantovnik, Vladimir B., Anderson-Cook, Christine M., Gürdal, Zafer, Watson, Layne T.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2003
Subjects
Composite structure
Genetic algorithm
Optimization
Response surface approximation
Response surface approximation
Genetic algorithm
Optimization
Composite structure
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Summary:This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both discrete and continuous design variables, multivariate approximation of the fitness function in terms of several continuous design variables, and localized search based on the multivariate approximation. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0045-7949
1879-2243
DOI:10.1016/S0045-7949(03)00253-0