Grosan C., Abraham A., Ishibuchi H. (eds.) Hybrid Evolutionary Algorithms

Springer, 2007. — 409 p.
Evolutionary computation has become an important problem solving methodology among many researchers working in the area of computational intelligence. The population-based collective learning process, self-adaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc. As we all know, the problems of the future will be more complicated in terms of complexity and data volume.
Hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. This edited volume is targeted to present the latest state-of-the-art methodologies in Hybrid Evolutionary Algorithms. This book deals with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. This volume comprises of 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.