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Multi-Objective Optimization Using Evolutionary Algorithms. Edition No. 1

  • ID: 2182378
  • Book
  • October 2008
  • 544 Pages
  • John Wiley and 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.

  1. Comrephensive coverage of this growing area of research.
  2. Carefully introduces each algorithm with examples and in-depth discussion.
  3. Includes many applications to real-world problems, including engineering design and scheduling.
  4. Includes discussion of advanced topics and future research.
  5. 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.

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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.
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Kalyanmoy Deb Indian Institute of Technology.
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