Agent-Based Evolutionary Search

1. Front Matter

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2. Agent Based Evolutionary Approach: An Introduction

   • Ruhul A. Sarker, Tapabrata Ray

   Pages 1-11

3. Multi-Agent Evolutionary Model for Global Numerical Optimization

   • Jing Liu, Weicai Zhong, Licheng Jiao

   Pages 13-48

4. An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints

   • Abu S. S. M. Barkat Ullah, Ruhul Sarker, Chris Lokan

   Pages 49-76

5. Multiagent-Based Approach for Risk Analysis in Mission Capability Planning

   • Lam T. Bui, Axel Bender, Michael Barlow, Hussein A. Abbass

   Pages 77-96

6. Agent Based Evolutionary Dynamic Optimization

   • Yang Yan, Shengxiang Yang, Dazhi Wang, Dingwei Wang

   Pages 97-116

7. Divide and Conquer in Coevolution: A Difficult Balancing Act

   • Hemant Kumar Singh, Tapabrata Ray

   Pages 117-138

8. Complex Emergent Behaviour from Evolutionary Spatial Animat Agents

   • K. A. Hawick, C. J. Scogings

   Pages 139-159

9. An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller

   • Ying Lin, Jun Zhang

   Pages 161-177

10. An Attempt to Stochastic Modeling of Memetic Systems

    • Aleksander Byrski, Robert Schaefer

    Pages 179-202

11. Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm

    • Kim Soon Gan, Patricia Anthony, Jason Teo, Kim On Chin

    Pages 203-228

12. PSO (Particle Swarm Optimization): One Method, Many Possible Applications

    • Federico Cecconi, Marco Campenní

    Pages 229-254

13. VISPLORE: Exploring Particle Swarms by Visual Inspection

    • Namrata Khemka, Christian Jacob

    Pages 255-284

14. Back Matter

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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.

• agents
• algorithm
• algorithms
• architecture
• calculus
• communication
• control
• evolution
• evolutionary algorithm
• genetic algorithms
• learning
• linear optimization
• model
• modeling
• optimization

• School of Engineering and Information Technology, University of New SouthWales , Canberra, ACT, Australia

  Ruhul Amin Sarker, Tapabrata Ray

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