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.
