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. Agent based evolutionary search is an emerging paradigm in computational int- ligence offering the potential to conceptualize and solve a variety of complex problems such as currency trading, production planning, disaster response m- agement, business process management etc. There has been a significant growth in the number of publications related to the development and applications of agent based systems in recent years which has prompted special issues of journals and dedicated sessions in premier conferences. The notion of an agent with its ability to sense, learn and act autonomously - lows the development of a plethora of efficient algorithms to deal with complex problems. This notion of an agent differs significantly from a restrictive definition of a solution in an evolutionary algorithm and opens up the possibility to model and capture emergent behavior of complex systems through a natural age- oriented decomposition of the problem space. While this flexibility of represen- tion offered by agent based systems is widely acknowledged, they need to be - signed for specific purposes capturing the right level of details and description. This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems. There are 12 peer reviewed chapters in this book authored by d- tinguished researchers who have shared their experience and findings spanning across a wide range of applications. Front Matter....Pages - Agent Based Evolutionary Approach: An Introduction....Pages 1-11 Multi-Agent Evolutionary Model for Global Numerical Optimization....Pages 13-48 An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints....Pages 49-76 Multiagent-Based Approach for Risk Analysis in Mission Capability Planning....Pages 77-96 Agent Based Evolutionary Dynamic Optimization....Pages 97-116 Divide and Conquer in Coevolution: A Difficult Balancing Act....Pages 117-138 Complex Emergent Behaviour from Evolutionary Spatial Animat Agents....Pages 139-159 An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller....Pages 161-177 An Attempt to Stochastic Modeling of Memetic Systems....Pages 179-202 Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm....Pages 203-228 PSO (Particle Swarm Optimization): One Method, Many Possible Applications....Pages 229-254 VISPLORE : Exploring Particle Swarms by Visual Inspection....Pages 255-284 Back Matter....Pages - Presenting state-of-the-art theory and practice in the field, this volume includes novel frameworks, a convergence and complexity analysis, and real-world applications. It aims to raise awareness of this effective method of enhancing evolutionary algorithms
