Multimodal Optimization by Means of Evolutionary Algorithms

✍ Scribed by Mike Preuss (auth.)

• Publisher: Springer International Publishing
• Year: 2015
• Tongue: English
• Leaves: 206
• Series: Natural Computing Series
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xx
Introduction: Towards Multimodal Optimization....Pages 1-25
Experimentation in Evolutionary Computation....Pages 27-54
Groundwork for Niching....Pages 55-73
Basin Identification by Means of Nearest-Better Clustering....Pages 75-114
Niching Methods and Multimodal Optimization Performance....Pages 115-137
Nearest-Better-Based Niching....Pages 139-170
Summary and Final Remarks....Pages 171-175
Back Matter....Pages 177-189

✦ Subjects

Algorithm Analysis and Problem Complexity; Computational Intelligence; Optimization