Evolutionary Optimization

Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

The use of evolutionary computation techniques has grown considerably. The use and applications of these techniques have been further enhanced resulting in a set of computational intelligence tools that are adept for solving complex optimization problems. This book covers the foundation as well as the practical side of evolutionary optimization.

Conventional Optimization Techniques.- Evolutionary Computation.- Single Objective Optimization.- Evolutionary Algorithms and Constrained Optimization.- Constrained Evolutionary Optimization.- Multi-Objective Optimization.- Evolutionary Multi-Objective Optimization: A Critical Review.- Multi-Objective Evolutionary Algorithms for Engineering Shape Design.- Assessment Methodologies for Multiobjective Evolutionary Algorithms.- Hybrid Algorithms.- Utilizing Hybrid Genetic Algorithms.- Using Evolutionary Algorithms to Solve Problems by Combining Choices of Heuristics.- Constrained Genetic Algorithms and Their Applications in Nonlinear Constrained Optimization.- Parameter Selection in EAs.- Parameter Selection.- Application of EAs to Practical Problems.- Design of Production Facilities Using Evolutionary Computing.- Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems.- Application of EAs to Theoretical Problems.- Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions.- A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems.- Using Genetic Algorithms to Find Good K-Tree Subgraphs.