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Multimodal Optimization by Means of Evolutionary Algorithms [electronic resource] / by Mike Preuss.

By: Preuss, Mike [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Natural Computing Series: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XX, 189 p. 42 illus., 5 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319074078.Subject(s): Computer science | Algorithms | Mathematical optimization | Computational intelligence | Computer Science | Algorithm Analysis and Problem Complexity | Computational Intelligence | OptimizationAdditional physical formats: Printed edition:: No titleDDC classification: 005.1 Online resources: Click here to access online
Contents:
Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching.
In: Springer eBooksSummary: 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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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.

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