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Chapter 11

Evolution Strategies and Genetic Algorithms and their Parallelisation for Structural Optimization: Part 1 Fundamentals

doi:10.4203/csets.4.11

J. Cai, G. Thierauf, "Evolution Strategies and Genetic Algorithms and their Parallelisation for Structural Optimization: Part 1 Fundamentals", in B.H.V. Topping, L. Lämmer, (Editors), "High Performance Computing for Computational Mechanics", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 11, pp 187-194, 2000. doi:10.4203/csets.4.11

Abstract

In the first part of this paper, the basic concepts of two search methods in structural optimization, genetic algorithms and evolution strategies, are introduced. These methods require only information of function-values. Because of their simple search mechanisms, they are well suited for wide classes of optimization problems . The increasing availability of high-speed and parallel computing caused a renewed interest in these zero-order methods, in particular in Monte-Carlo techniques, genetic algorithms and evolution strategies, which are described herein.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Science, Engineering & Technology Series ISSN 1759-3158 CSETS: 4 HIGH PERFORMANCE COMPUTING FOR COMPUTATIONAL MECHANICS Edited by: B.H.V. Topping, L. Lämmer Chapter 11 Evolution Strategies and Genetic Algorithms and their Parallelisation for Structural Optimization: Part 1 Fundamentals J. Cai and G. Thierauf University of Essen, Germany doi:10.4203/csets.4.11 purchase the full-text of this chapter Full Bibliographic Reference for this chapter J. Cai, G. Thierauf, "Evolution Strategies and Genetic Algorithms and their Parallelisation for Structural Optimization: Part 1 Fundamentals", in B.H.V. Topping, L. Lämmer, (Editors), "High Performance Computing for Computational Mechanics", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 11, pp 187-194, 2000. doi:10.4203/csets.4.11 Abstract In the first part of this paper, the basic concepts of two search methods in structural optimization, genetic algorithms and evolution strategies, are introduced. These methods require only information of function-values. Because of their simple search mechanisms, they are well suited for wide classes of optimization problems . The increasing availability of high-speed and parallel computing caused a renewed interest in these zero-order methods, in particular in Monte-Carlo techniques, genetic algorithms and evolution strategies, which are described herein. purchase the full-text of this chapter (price £20) go to the previous chapter go to the next chapter return to the table of contents return to the book description purchase this book (price £75 +P&P)
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