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Agent-Based Evolutionary Search

The performance of Evolutionary Algorithms can be enhanced by integrating the concept of agents. Agents and Multi-agents can bring many interesting features which are beyond the scope of traditional evolutionary process and learning. This book presents the state-of-the art in the theory and practice...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Sarker, Ruhul A. (Editor ), Ray, Tapabrata (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
Edición:1st ed. 2010.
Colección:Adaptation, Learning, and Optimization, 5
Temas:
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Agent-Based Evolutionary Search  |h [electronic resource] /  |c edited by Ruhul A. Sarker, Tapabrata Ray. 
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490 1 |a Adaptation, Learning, and Optimization,  |x 1867-4542 ;  |v 5 
505 0 |a Agent Based Evolutionary Approach: An Introduction -- Multi-Agent Evolutionary Model for Global Numerical Optimization -- An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints -- Multiagent-Based Approach for Risk Analysis in Mission Capability Planning -- Agent Based Evolutionary Dynamic Optimization -- Divide and Conquer in Coevolution: A Difficult Balancing Act -- Complex Emergent Behaviour from Evolutionary Spatial Animat Agents -- An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller -- An Attempt to Stochastic Modeling of Memetic Systems -- Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm -- PSO (Particle Swarm Optimization): One Method, Many Possible Applications -- VISPLORE: Exploring Particle Swarms by Visual Inspection. 
520 |a The performance of Evolutionary Algorithms can be enhanced by integrating the concept of agents. Agents and Multi-agents can bring many interesting features which are beyond the scope of traditional evolutionary process and learning. This book presents the state-of-the art in the theory and practice of Agent Based Evolutionary Search and aims to increase the awareness on this effective technology. This includes novel frameworks, a convergence and complexity analysis, as well as real-world applications of Agent Based Evolutionary Search, a design of multi-agent architectures and a design of agent communication and learning Strategy. 
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