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Multimodal optimization by means of evolutionary algorithms

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multimodal optimization by means of evolutionary algorithms/ by Mike Preuss.
Author:	Preuss, Mike.
Published:	Cham :Imprint: Springer, : 2015.,
Description:	xx, 189 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Optimization. -
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 :Imprint: Springer,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:

669174
Optimization.

LC Class. No.: QA76.618

Dewey Class. No.: 005.1

Multimodal optimization by means of evolutionary algorithms
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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.
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650 2 4 $a Algorithm Analysis and Problem Complexity. $3 593923
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