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More About This Title Multi-Objective Optimization Using EvolutionaryAlgorithms
English
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.
English
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.
English
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.
