Research Catalog

Biologically-inspired optimisation methods : parallel algorithms, systems and applications

Title: Biologically-inspired optimisation methods : parallel algorithms, systems and applications / Andrew Lewis, Sanaz Mostaghim, and Marcus Randall, eds.
Publication: Berlin : Springer, ©2009.

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Status	Format	Access	Call Number	Item Location
Request for On-site Use Request Scan How do I pick up this item and when will it be ready?	Text	Use in library	QA402.5 .B56 2009	Off-site

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Description

xii, 360 pages : illustrations; 25 cm

Series Statement

Studies in computational intelligence ; v. 210

Uniform Title

Studies in computational intelligence ; v. 210.

Alternative Title

Biologically inspired optimisation methods

Subject

Note

Also available online.

Bibliography (note)

Includes bibliographical references and indexes.

Contents

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.

ISBN

9783642012617
3642012612
3642101771
9783642101779
9783642012624
3642012620

OCLC

ocn320198167
320198167
SCSB-9200936

Owning Institutions

Princeton University Library