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J. Cai, G. Thierauf, "Genetic Algorithms and Evolution Strategies - Comparison and Combination", in B.H.V. Topping, (Editor), "Parallel and Distributed Processing for Computational Mechanics: Systems and Tools", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 18, pp 315-328, 1999. doi:10.4203/csets.2.18

Abstract

With the increasing availability of high-speed and parallel computing, the zero order optimization methods, which require only information of function-values, and in particular the random search techniques have gained renewed interest. Among these methods, the Darwinian methods, which imitate certain principles of biological evolution - e.g. selection, mutation and crossover - are particularly suited for parallel computing. The first variant, the genetic algorithms (GAS), are based on the coding of selected "genetic" information; the second variant, the evolution strategy (ESs) can be compared with a stochastic adaptation on the phenotype-level.

In this paper the basic concepts and a comparison of GAS and ESs are described. By introducing a variable coding technique, a parallel optimization method based on a combination of GAS and ESs is presented. The advantages of both GAS and ESs, like coding of genetic information and adaptation of optimization parameters, are enhanced by this new method.

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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 18 Genetic Algorithms and Evolution Strategies - Comparison and Combination J. Cai and G. Thierauf Department of Civil Engineering, University of Essen, Germany doi:10.4203/csets.2.18 purchase the full-text of this chapter Full Bibliographic Reference for this chapter J. Cai, G. Thierauf, "Genetic Algorithms and Evolution Strategies - Comparison and Combination", in B.H.V. Topping, (Editor), "Parallel and Distributed Processing for Computational Mechanics: Systems and Tools", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 18, pp 315-328, 1999. doi:10.4203/csets.2.18 Abstract With the increasing availability of high-speed and parallel computing, the zero order optimization methods, which require only information of function-values, and in particular the random search techniques have gained renewed interest. Among these methods, the Darwinian methods, which imitate certain principles of biological evolution - e.g. selection, mutation and crossover - are particularly suited for parallel computing. The first variant, the genetic algorithms (GAS), are based on the coding of selected "genetic" information; the second variant, the evolution strategy (ESs) can be compared with a stochastic adaptation on the phenotype-level. In this paper the basic concepts and a comparison of GAS and ESs are described. By introducing a variable coding technique, a parallel optimization method based on a combination of GAS and ESs is presented. The advantages of both GAS and ESs, like coding of genetic information and adaptation of optimization parameters, are enhanced by this new method. 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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