Band 210
Biologically-Inspired Optimisation Methods Parallel Algorithms, Systems and Applications
Aus der Reihe
Studies in Computational Intelligence
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- Hardcover
- Taschenbuch ausgewählt
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Sprache:Englisch
Fr. 277.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
28.10.2010
Herausgeber
Andrew Lewis + weitereVerlag
Springer BerlinSeitenzahl
360
Maße (L/B/H)
23.5/15.5/2.1 cm
Gewicht
569 g
Auflage
Softcover reprint of hardcover 1st ed. 2009
Sprache
Englisch
ISBN
978-3-642-10177-9
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.
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