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Multimodal optimization by means of ...

Preuss, Mike.

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Multimodal optimization by means of evolutionary algorithms

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimodal optimization by means of evolutionary algorithms/ by Mike Preuss.
Author:	Preuss, Mike.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xx, 189 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching.
Contained By:	Springer eBooks
Subject:	Evolutionary programming (Computer science) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-07407-8
ISBN:	9783319074078

Multimodal optimization by means of evolutionary algorithms
Preuss, Mike.

Multimodal optimization by means of evolutionary algorithms[electronic resource] /by Mike Preuss. - Cham :Springer International Publishing :2015. - xx, 189 p. :ill., digital ;24 cm. - Natural computing series,1619-7127. - Natural computing series..

Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching.

This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.

ISBN: 9783319074078

Standard No.: 10.1007/978-3-319-07407-8doiSubjects--Topical Terms:

568531
Evolutionary programming (Computer science)

LC Class. No.: QA76.618

Dewey Class. No.: 005.1

Multimodal optimization by means of evolutionary algorithms
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100 1 $a Preuss, Mike. $3 2162360
245 1 0 $a Multimodal optimization by means of evolutionary algorithms $h [electronic resource] / $c by Mike Preuss.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xx, 189 p. : $b ill., digital ; $c 24 cm.
490 1 $a Natural computing series, $x 1619-7127
505 0 $a Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching.
520 $a This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.
650 0 $a Evolutionary programming (Computer science) $3 568531
650 0 $a Evolutionary computation. $3 582189
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 891007
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Optimization. $3 891104
710 2 $a SpringerLink (Online service) $3 836513
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830 0 $a Natural computing series. $3 2057566
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-07407-8
950 $a Computer Science (Springer-11645)