Multi-Objective Optimization using Evolutionary Algorithms
(Sprache: Englisch)
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...
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Klappentext zu „Multi-Objective Optimization using Evolutionary Algorithms “
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.
Inhaltsverzeichnis zu „Multi-Objective Optimization using Evolutionary Algorithms “
ForewordPreface
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
Bibliographische Angaben
- Autor: Kalyanmoy Deb
- 2001, 1. Auflage, XX, 498 Seiten, 319 Abbildungen, Maße: 25,3 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 047187339X
- ISBN-13: 9780471873396
Sprache:
Englisch
Rezension zu „Multi-Objective Optimization using Evolutionary Algorithms “
"...discusses two multi-objective optimization procedures, namely the ideal procedure and the preference-based one." (Zentralblatt MATH, Vol. 970, 2001/20)
Pressezitat
"Deb's book is complete, eminently readable, and the coverage is scholarly and thorough. It is my pleasure and duty to urge you to buy this book, read it, use it and enjoy it." (David E. Goldberg, University of Illinois at Urbana-Champaign, USA)"...discusses two multi-objective optimization procedures, namely the ideal procedure and the preference-based one." (Zentralblatt MATH, Vol. 970, 2001/20)
Excerpt from Preface: "...provides an extensive discussion on the principles of multi-objective optimization and on a number of classical approaches." (Mathematical Reviews, 2002)
"...As a survey, this book is exemplary and forms an essential resource for EMO researchers at the present time." (Siam Review, Vol.44, No.3, 2002)
"...a readable account of a topic of current interest in operational research." (Mathematika, No.48, 2001)
"...an outstandingly well-organized and clearly written account of the subject" (The Mathematical Gazette, July 2003)
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