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.

Evolutionary computation, a broad field that includes genetic algorithms, evolution strategies, and evolutionary programming, has proven to offer well-suited techniques for industrial and management tasks - therefore receiving considerable attention from scientists and engineers during the last decade. This monograph develops and analyzes evolutionary algorithms that can be successfully applied to real-world problems such as robotic control. Although of particular interest to robotic control engineers, Evolutionary Computations also may interest the large audience of researchers, engineers, designers and graduate students confronted with complicated optimization tasks.

This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.

This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.

This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.

Most real world optimization problems, and their corresponding models, are complex. This complexity arises from different sources, such as existence of the constraints, function characteristics, and high dimensionality. Evolutionary Algorithms (EAs) and specially Genetic Algorithms (GAs) have proven themselves as efficient optimization techniques over the last two decades; as they have the ability to overcome the drawbacks of conventional optimization methods. Therefore, this thesis addresses the GAs as a solution methodology for solving Constrained Optimization Problems (COPs). In COPs for practical applications, it is more likely that the optimal solution lies on the feasible region boundary. Utilizing this feature, this thesis introduces a new genetic algorithm for solving small-scale COPs. A new ranking and selection scheme is introduced in conjunction with both a new crossover method based on three parents, and a mixed mutation between two currently existing mutation methodologies. A number of well known benchmark problems have been solved and compared with the state of the art algorithms, and the proposed algorithm shows a competitive and even superior performance for some problems. In addition, a detailed parametric analysis is provided to show the individual effect of each of the proposed components. Furthermore, this thesis introduces another algorithm that breaks down the complexity of the constrained optimization process into smaller dimensions. Every sub-component of the algorithm maintains a part of the problem, and the whole problem optimization is treated through a designed communication process. This algorithm deals with a special problem structure, in which the problems are entirely or almost decomposable based on what is called in the literature as a block angular structure. The proposed algorithm decomposes the constraints as well as the chromosomes. It facilitates solving such problems both with and without overlapping variables between the sub-components. Some experiments have been carried out to show how the designed communication process controls the optimization and what the best parameter settings are. Then, the algorithm has been implemented in a parallel environment on a scalable practical test problem and using this shows how the proposed algorithm outperforms the other single population based algorithms in higher dimensions.