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Network-based distributed planning using coevolutionary algorithms /

In this book, efficient and scalable coevolutionary algorithms for distributed, network-based decision-making, which utilize objective functions are developed in a networked environment where internode communications are a primary factor in system performance. A theoretical foundation for this class...

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Bibliographic Details
Main Authors: Subbu, Raj
Corporate Authors: World Scientific Firm
Group Author: Sanderson, A. C. Arthur C
Published: World Scientific Pub. Co.,
Publisher Address: Singapore ; River Edge, N.J. :
Publication Dates: 2004.
Literature type: eBook
Language: English
Series: Series in intelligent control and intelligent automation ; v. 13
Subjects:
Electronic data processing > Distributed processing
Algorithms
Intelligent agents Computer software
Electronic books
Online Access: http://www.worldscientific.com/worldscibooks/10.1142/5470#t=toc
Summary: In this book, efficient and scalable coevolutionary algorithms for distributed, network-based decision-making, which utilize objective functions are developed in a networked environment where internode communications are a primary factor in system performance. A theoretical foundation for this class of coevolutionary algorithms is introduced using techniques from stochastic process theory and mathematical analysis. A case study in distributed, network-based decision-making presents an implementation and detailed evaluation of the coevolutionary decision-making framework that incorporates dis
Carrier Form: 1 online resource (xix,172pages) : illustrations.
Bibliography: Includes bibliographical references (pages 159-168) and index.
ISBN: 9789812794857 (electronic bk.)
CLC: TP393
Contents: 1. Introduction. 1.1. Motivation. 1.2. Approach. 1.3. Principal contributions. 1.4. Book outline -- 2. Background and related work. 2.1. Collaborative manufacturing. 2.2. Combinatorial optimization. 2.3. Evolutionary algorithms. 2.4. Agents. 2.5. Distributed problem solving -- 3. Problem formulation and analysis. 3.1. Introduction. 3.2. General problem formulation. 3.3. Printed circuit assembly problem. 3.4. Algorithm applicability analysis -- 4. Theory and analysis of evolutionary optimization. 4.1. Introduction. 4.2. Theoretical foundation. 4.3. Convergence analysis -- 5. Theory and analys

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