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 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. TOC:Agent Based Evolutionary Search: 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.