Multimodal Optimization by Means of Evolutionary Algorithms
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Beschreibung
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.
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.
Describes state of the art in algorithms, measures and test problems Approaches multimodal optimization algorithms via model-based simulation and statistics Valuable for practitioners with real-world black-box problems
Autor*in
Mike Preuss
Themen in »Multimodal Optimization by Means of Evolutionary Algorithms«
Evolutionary algorithms Evolutionary computing Experimental analysis Multimodal optimization Niching Optimization algorithm analysis and problem complexity
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“It provides an excellent explanation of the theoretical background of many topics in evolutionary computation … . I strongly recommend this book for graduate students or any researcher who wants to work in the EC field … . It also may help in improving some algorithms and may motivate the researcher to introduce new ones. … the chapters are self-contained so that you can read individual chapters that you are interested in without the need to read the whole book.” (Nailah Al-Madi, Genetic Programming and Evolvable Machines, Vol. 17 (3), September, 2016)
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