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Multimodal Optimization by Means of Evolutionary Algorithms

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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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Availability

No copy data

Detail Information

Series Title

Natural Computing Series

Call Number

-

Publisher

: ., 2015

Collation

XX, 189

Language

English

ISBN/ISSN

978-3-319-07406-1

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Algorithms
, Computational Intelligence
, Optimization

Specific Detail Info

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Statement of Responsibility

Mike Preuss

Other Information

Cataloger

Hamdani

Source

-

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