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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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Subbu, Raj
Otros Autores: Sanderson, A. C. (Arthur C.)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore : World Scientific Publishing Company, 2004.
Colección:Series in intelligent control and intelligent automation.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Subbu, Raj. 
245 1 0 |a Network-Based Distributed Planning Using Coevolutionary Algorithms. 
260 |a Singapore :  |b World Scientific Publishing Company,  |c 2004. 
300 |a 1 online resource (193 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Series in Intelligent Control and Intelligent Automation 
588 0 |a Print version record. 
520 |a 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 distr. 
504 |a Includes bibliographical references (pages 159-168) and index. 
505 0 |a 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 analysis of distributed coevolutionary optimization. 5.1. Introduction. 5.2. Theory. 5.3. Computational delay analysis -- 6. Performance evaluation based on ideal objectives. 6.1. Introduction. 6.2. Gaussian objectives. 6.3. Planar tile layout problems. 6.4. Design-supplier-manufacturing problem -- 7. Coevolutionary virtual design environment. 7.1. Introduction. 7.2. Application domain. 7.3. Evolutionary optimization. 7.4. Simulation environments -- 8. Evaluation and analysis. 8.1. Introduction. 8.2. Nature and evolution of planning decisions. 8.3. Strategy for performance evaluation. 8.4. Performance evaluation. 8.5. Applicability analysis of the frameworks -- 9. Conclusions. 9.1. Summary. 9.2. Future work. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Electronic data processing  |x Distributed processing. 
650 0 |a Algorithms. 
650 0 |a Intelligent agents (Computer software) 
650 2 |a Algorithms 
650 6 |a Traitement réparti. 
650 6 |a Algorithmes. 
650 6 |a Agents intelligents (Logiciels) 
650 7 |a algorithms.  |2 aat 
650 7 |a Algorithms  |2 fast 
650 7 |a Electronic data processing  |x Distributed processing  |2 fast 
650 7 |a Intelligent agents (Computer software)  |2 fast 
700 1 |a Sanderson, A. C.  |q (Arthur C.) 
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830 0 |a Series in intelligent control and intelligent automation. 
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