Multi-Objective Optimization using Evolutionary Algorithms

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John Wiley & Sons, Jul 5, 2001 - Mathematics - 518 pages
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many 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.
  • 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
  • Includes discussion of advanced topics and future research
  • Can be used as a course text or for self-study
  • Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms

The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

 

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Contents

MultiObjective Optimization
13
Classical Methods
49
Evolutionary Algorithms
81
NonElitist MultiObjective Evolutionary Algorithms
171
Exercise Problems
235
Constrained MultiObjective Evolutionary Algorithms
289
Exercise Problems
314
Applications of MultiObjective Evolutionary Algorithms
447
Epilogue
481
References
489
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Common terms and phrases

Popular passages

Page 508 - Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: Empirical results.
Page 489 - T 1996 Evolutionary Algorithms in Theory and Practice (New York: Oxford University Press...
Page 494 - Fogel, LJ., Angeline, PJ and Fogel, DB (1995). An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines.

About the author (2001)

Kalyanmoy Deb is an Indian computer scientist. Since 2013, Deb has held the Herman E. & Ruth J. Koenig Endowed Chair in the Department of Electrical and Computing Engineering at Michigan State University, which was established in 2001.

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