Multimodal Optimization By Means Of Evolutionary Algorithms (natural Computing Series)
by Mike Preuss /
2015 / English / PDF
6.2 MB Download
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.
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 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.
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.
