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Multimodal optimization by means of ...

Preuss, Mike.

Multimodal optimization by means of evolutionary algorithms[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	005.1
書名/作者:	Multimodal optimization by means of evolutionary algorithms/ by Mike Preuss.
作者:	Preuss, Mike.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xx, 189 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Computer Science.
標題:	Algorithm Analysis and Problem Complexity.
標題:	Computational Intelligence.
標題:	Optimization.
標題:	Evolutionary programming (Computer science)
標題:	Evolutionary computation.
ISBN:	9783319074078
ISBN:	9783319074061
內容註:	Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-07407-8

Multimodal optimization by means of evolutionary algorithms[electronic resource] /
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:

423143
Computer Science.

LC Class. No.: QA76.618

Dewey Class. No.: 005.1

Multimodal optimization by means of evolutionary algorithms[electronic resource] /
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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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