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Multimodal Optimization by Means of Evolutionary Algorithms (Hardcover, 1st ed. 2015)
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Multimodal Optimization by Means of Evolutionary Algorithms (Hardcover, 1st ed. 2015)
Series: Natural Computing Series
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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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