Biologically-Inspired Optimisation Methods

Andrew Lewis

ISBN 10: 3642101771 / ISBN 13: 9783642101779

Published by Springer, Springer Okt 2010, 2010

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Evolution's Niche in Multi-Criterion Problem Solving.- Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization.- Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments.- Dynamic Problems and Nature Inspired Meta-heuristics.- Relaxation Labelling Using Distributed Neural Networks.- Extremal Optimisation for Assignment Type Problems.- Niching for Ant Colony Optimisation.- Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas.- The Radio Network Design Optimization Problem.- Strategies for Decentralised Balancing Power.- An Analysis of Dynamic Mutation Operators for Conformational Sampling.- Evolving Computer Chinese Chess Using Guided Learning.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 372 pp. Englisch.

Seller Inventory # 9783642101779

Bibliographic Details

• Title: Biologically-Inspired Optimisation Methods
• Publisher: Springer, Springer Okt 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 3642101771
• ISBN 13: 9783642101779
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 21D millimeters 235 millimeters width by 155 millimeters height by 21 millimeters depth
• Weight: 563 grams

About this title

Synopsis

This book covers the latest theories, applications and techniques in Biologically-Inspired Optimisation Methods. Many chapters derive from studies presented at workshops and international conferences on e-Science, Grid Computing and Evolutionary computation.

From the Back Cover

Humanity has often turned to Nature for inspiration to help it solve its problems.  The systems She provides are often based on simple rules and premises, yet are able to adapt to new and complex environments quickly and efficiently.  Problems from a range of human endeavours, including, science, engineering and economics, require us to find good quality solutions in exponentially large search spaces, a task that often requires vast amounts computational resources and effort.  In this book, the contributing authors solve these problems by modelling aspects of the natural world, from the flocking of birds and fish, the operation of colonies of ants through to chromosome reproduction and beyond.  Many of the contributions represent extended studies of work presented at a number of workshops on Biologically-Inspired Optimisation Methods at international conferences on e-Science, Grid Computing, and Evolutionary Computation.  A variety of chapters from some of the leading experts in the field present an overview of the state-of-the-art, recent advances in theoretical and practical ideas and techniques, and details of application of these methods to a range of benchmark and real world problems.