Evolutionary Algorithms for Solving Multi-objective Problems (Genetic Algorithms and Evolutionary Computation) / Coello Coello, Carlos A./ Veldhuizen, David A.van/ Lamont, Gary B.

多目的問題解決のための進化アルゴリズム<br>Evolutionary Algorithms for Solving Multi-objective Problems (Genetic Algorithms and Evolutionary Computation)

多目的問題解決のための進化アルゴリズム
Evolutionary Algorithms for Solving Multi-objective Problems (Genetic Algorithms and Evolutionary Computation)

Coello Coello, Carlos A./ Veldhuizen, David A.van/ Lamont, Gary B.

Kluwer Academic / Plenum Publishers（2002/06発売）

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製本 Hardcover:ハードカバー版／ページ数 610 p.
言語 ENG
商品コード 9780306467622
DDC分類 005.1

基本説明

1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics.

Full Description

The solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications.
Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter.

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.