edited by Kay Chen Tan (National University of Singapore, Singapore) , Meng Hiot Lim (Nanyang Technological University, Singapore) , Xin Yao (University of Birmingham, UK) , & Lipo Wang (Nanyang Technological University, Singapore)

Table of Contents (21k)
Preface (10k)
Chapter 1: Co-Evolutionary Learning in Strategic Environments (231k)

Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances in the theories, algorithms, and applications of simulated evolution and learning techniques. It provides insights into different evolutionary computation techniques and their applications in domains such as scheduling, control and power, robotics, signal processing, and bioinformatics. The book will be of significant value to all postgraduates, research scientists and practitioners dealing with evolutionary computation or complex real-world problems.

This book has been selected for coverage in:

• Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)

• CC Proceedings — Engineering & Physical Sciences

• Evolutionary Theory:Using Evolution to Learn User Preferences (S Ujjin & P J Bentley)
  • Evolutionary Learning Strategies for Artificial Life Characters (M L Netto et al.)
  • The Influence of Stochastic Quality Functions on Evolutionary Search (B Sendhoff et al.)
  • A Real-Coded Cellular Genetic Algorithm Inspired by Predator–Prey Interactions (X Li & S Sutherland)
  • Automatic Modularization with Speciated Neural Network Ensemble (V R Khare & X Yao)
• Evolutionary Applications:
  • Image Classification using Particle Swarm Optimization (M G Omran et al.)
  • Evolution of Fuzzy Rule Based Controllers for Dynamic Environments (J Riley & V Ciesielski)
  • A Genetic Algorithm for Joint Optimization of Spare Capacity and Delay in Self-Healing Network (S Kwong & H W Chong)
  • Joint Attention in the Mimetic Context — What is a “Mimetic Same”? (T Shiose et al.)
  • Time Series Forecast with Elman Neural Networks and Genetic Algorithms (L X Xu et al.)
  • and other articles