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ISBN:3642082823
Author: Zbigniew Michalewicz,Dipankar Dasgupta
ISBN13: 978-3642082825
Title: Evolutionary Algorithms in Engineering Applications
Format: lit rtf docx txt
ePUB size: 1121 kb
FB2 size: 1549 kb
DJVU size: 1517 kb
Language: English
Category: Computer Science
Publisher: Springer; Softcover reprint of hardcover 1st ed. 1997 edition (December 7, 2010)
Pages: 555

Evolutionary Algorithms in Engineering Applications by Zbigniew Michalewicz,Dipankar Dasgupta



Dasgupta Z. Michalewicz. Ed. Evolutionary Algorithms in Engineering Applications With 150 Figures and 61 Tables. Zbigniew Michalewicz University of North Carolina Department of Computer Science 9201 University City Boulevard Charlotte, NC 28223-0001, USA zbyszek. Dipankar Dasgupta and Zbigniew Michalewicz. For numerical optimization problems, optimize f(X), X (Xl,. 9j(X) ~. 0, for j . 1. .q, and. hj(X) . 0, for j q+ 1.

eBook 118,99 €. price for India (gross). This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. The volume consists of five parts, each with four or five chapters. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains

They are appealing because they are simple. Read instantly in your browser. ISBN-13: 978-3540620211.

Evolutionary algorithms for constrained parameter optimization problems. Z Michalewicz, M Schoenauer. Evolutionary computation 4 (1), 1-32, 1996. Evolutionary computation 1: Basic algorithms and operators. T Bäck, DB Fogel, Z Michalewicz. Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. S Koziel, Z Michalewicz. Evolutionary computation 7 (1), 19-44, 1999. An experimental comparison of binary and floating point representations in genetic algorithms. CZ Janikow, Z Michalewicz. Evolutionary algorithms in engineering applications. D Dasgupta, Z Michalewicz. Springer Science & Business Media, 2013. A survey of constraint handling techniques in evolutionary computation methods. Evolutionary programming 4, 135-155, 1995.

Dipankar Dasgupta, Zbigniew Michalewicz. utionary Algorithms in Engineering A. html?hl ru&id g4urCAAAQBAJ. They are appealing because they are simple, easy to interface, and easy to extend.

This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering.

Evolutionary Algorithms in Engineering Applications. Zbigniew Michalewicz. Genetic algorithms are founded upon the principle of evolution, . survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control.

The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. Скачать (rar, . 5 Mb). Статьи партнёров.

In, Dasgupta, Dipankar and Michalewicz, Zbigniew (ed. Springer, pp. 377-407. Record type: Book Section. Published date: 1997. Local EPrints ID: 21233.

Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.