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Multimodal Optimization by Means of Evolutionary Algorithms

Multimodal Optimization by Means of Evolutionary Algorithms by Mike Preuss
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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.

Springer International Publishing; November 2015
206 pages; ISBN 9783319074078
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Title: Multimodal Optimization by Means of Evolutionary Algorithms
Author: Mike Preuss
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