Comprehensive coverage of this growing area of research. Carefully introduces each algorithm with examples and in-depth discussion. Includes many applications to real-world problems, including engineering design and scheduling. Accessible to those with limited knowledge of multi-objective optimization and evolutionary algorithms.

Foreword. Preface. Prologue. Multi-Objective Optimization. Classical Methods. Evolutionary Algorithms. Non-Elitist Multi-Objective Evolutionary Algorithms. Elitist Multi-Objective Evolutionary Algorithms. Constrained Multi-Objective Evolutionary Algorithms. Salient Issues of Multi-Objective Evolutionary Algorithms. Applications of Multi-Objective Evolutionary Algorithms. Epilogue. References. Index.

Multi-Objective Optimization using Evolutionary Algorithms Kalyanmoy Deb Indian Institute of Technology, Kanpur, India The Wiley Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians and scientists. Multi-Objective Optimization using Evolutionary Algorithms Kalyanmoy Deb Indian Institute of Technology, Kanpur, India Evolutionary algorithms are very powerful techniques used to find solutions to real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Comrephensive coverage of this growing area of research.Carefully introduces each algorithm with examples and in-depth discussion.Includes many applications to real-world problems, including engineering design and scheduling.Includes discussion of advanced topics and future research.Accessible to those with limited knowledge of multi-objective optimization and evolutionary algorithms Provides an extensive discussion on the principles of multi-objective optimization and on a number of classical approaches. This integrated presentation of theory, algorithms and examples will benefit those working in the areas of optimization, optimal design and evolutionary computing.