Biologically-Inspired Optimisation Methods - Parallel Algorithms, Systems and Applications (Paperback, Softcover reprint of hardcover 1st ed. 2009)

Throughout the evolutionary history of this planet, biological systems have been able to adapt, survive and ?ourish despite the turmoils and upheavals of the environment. This ability has long fascinated and inspired people to emulate and adapt natural processes for application in the arti?cial world of human endeavours. The realm of optimisation problems is no exception. In fact, in recent years biological systems have been the inspiration of the majority of meta-heuristic search algorithms including, but not limited to, genetic algorithms, particle swarmoptimisation, ant colony optimisation and extremal optimisation. This book presentsa continuum ofbiologicallyinspired optimisation, from the theoretical to the practical. We begin with an overview of the ?eld of biologically-inspired optimisation, progress to presentation of theoretical analysesandrecentextensionstoavarietyofmeta-heuristicsand?nallyshow application to a number of real-worldproblems. As such, it is anticipated the book will provide a useful resource for reseachers and practitioners involved in any aspect of optimisation problems. The overviewof the ?eld is provided by two works co-authored by seminal thinkers in the ?eld. Deb's "Evolution's Niche in Multi-Criterion Problem Solving," presents a very comprehensive and complete overview of almost all major issues in Evolutionary Multi-objective Optimisation (EMO). This chapter starts with the original motivation for developing EMO algorithms and provides an account of some successful problem domains on which EMO has demonstrated a clear edge over their classical counterparts.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Computational Intelligence, 210
Release date:	October 2010
First published:	2009
Editors:	Andrew Lewis • Sanaz Mostaghim • Marcus Randall
Dimensions:	235 x 155 x 19mm (L x W x T)
Format:	Paperback
Pages:	360
Edition:	Softcover reprint of hardcover 1st ed. 2009
ISBN-13:	978-3-642-10177-9
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-642-10177-1
Barcode:	9783642101779

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