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Agent-Based Evolutionary Search [electronic resource] / edited by Ruhul Amin Sarker, Tapabrata Ray.

By: Sarker, Ruhul Amin [editor.].
Contributor(s): Ray, Tapabrata [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Adaptation, Learning, and Optimization: 5Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: 291p. 48 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642134258.Subject(s): Engineering | Artificial intelligence | Mathematics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Applications of MathematicsDDC classification: 519 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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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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.

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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