Multi-Objective Optimization Using Evolutionary Algorithms
1. Auflage Oktober 2008
544 Seiten, Softcover
Wiley & Sons Ltd
The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and scientists.
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.
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.