Description
Book SynopsisEvolutionary 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
Trade Review
"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)
Table of ContentsForeword xv
Preface xvii
1 Prologue 1
2 Multi-Objective Optimization 13
3 Classical Methods 49
4 Evolutionary Algorithms 81
5 Non-Elitist Multi-Objective Evolutionary Algorithms 171
6 Elitist Multi-Objective Evolutionary Algorithms 239
7 Constrained Multi-Objective Evolutionary Algorithms 289
8 Salient Issues of Multi-Objective Evolutionary Algorithms 315
9 Applications of Multi-Objective Evolutionary Algorithms 447
10 Epilogue 481
References 489
Index 509
