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G. Thierauf, J. Cai, "Evolution Strategies - Parallelisation and Application in Engineering Optimization", in B.H.V. Topping, (Editor), "Parallel and Distributed Processing for Computational Mechanics: Systems and Tools", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 19, pp 329-349, 1999. doi:10.4203/csets.2.19

Abstract

In recent years, research ancl application of a class of stochastic search methods for engineering optimization problems, like genetic algorithms and evolution strategies, have increased and gained wide acceptance. These zero-order and direct methods work with simple search mechanisms and do not require any gradient or second derivative information of the objective function and constraints. Hence, these methods can he easily used for different optimization problems. Another characteristic of these methocls is that they work with a population of design points simultaneously. This allolvs a parallel implementation of the methods. In this paper, the basic concepts of evolution strategies and their parallelization and application in engineering optimization are introduced.

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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: 2 PARALLEL AND DISTRIBUTED PROCESSING FOR COMPUTATIONAL MECHANICS: SYSTEMS AND TOOLS Edited by: B.H.V. Topping Chapter 19 Evolution Strategies - Parallelisation and Application in Engineering Optimization G. Thierauf and J. Cai Department of Civil Engineering, University of Essen, Germany doi:10.4203/csets.2.19 purchase the full-text of this chapter Full Bibliographic Reference for this chapter G. Thierauf, J. Cai, "Evolution Strategies - Parallelisation and Application in Engineering Optimization", in B.H.V. Topping, (Editor), "Parallel and Distributed Processing for Computational Mechanics: Systems and Tools", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 19, pp 329-349, 1999. doi:10.4203/csets.2.19 Abstract In recent years, research ancl application of a class of stochastic search methods for engineering optimization problems, like genetic algorithms and evolution strategies, have increased and gained wide acceptance. These zero-order and direct methods work with simple search mechanisms and do not require any gradient or second derivative information of the objective function and constraints. Hence, these methods can he easily used for different optimization problems. Another characteristic of these methocls is that they work with a population of design points simultaneously. This allolvs a parallel implementation of the methods. In this paper, the basic concepts of evolution strategies and their parallelization and application in engineering optimization are introduced. 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 £95 +P&P)
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