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Full Description
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.
Contents
Foreword 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
