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" Evolutionary Algorithms for Solving Multi-Objective Problems "
by Carlos A. Coello Coello, David A. Veldhuizen, Gary B. Lamont.
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BL
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| Record Number
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726428
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b546160
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| Main Entry
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by Carlos A. Coello Coello, David A. Veldhuizen, Gary B. Lamont.
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| Title & Author
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Evolutionary Algorithms for Solving Multi-Objective Problems\ by Carlos A. Coello Coello, David A. Veldhuizen, Gary B. Lamont.
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| Publication Statement
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Boston, MA: Springer US, 2002
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| Series Statement
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Genetic algorithms and evolutionary computation, 5.
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(xxxv, 576 pages)
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| ISBN
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1475751842
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: 9781475751840
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| Contents
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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 --;10. Epilog --;Appendix A: Moea Classification and Technique Analysis --;1 Introduction --;1.1 Mathematical Notation --;1.2 Presentation Layout --;2.1 Lexicographic Techniques --;2.2 Linear Fitness Combination Techniques --;2.3 Nonlinear Fitness Combination Techniques --;2.3.1 Multiplicative Fitness Combination Techniques --;2.3.2 Target Vector Fitness Combination Techniques --;2.3.3 Minimax Fitness Combination Techniques --;3 Progressive MOEA Techniques --;4.1 Independent Sampling Techniques --;4.2 Criterion Selection Techniques --;4.3 Aggregation Selection Techniques --;4.4 Pareto Sampling Techniques --;4.4.1 Pareto-Based Selection --;4.4.2 Pareto Rank- and Niche-Based Selection --;4.4.3 Pareto Deme-Based Selection --;4.4.4 Pareto Elitist-Based Selection --;4.5 Hybrid Selection Techniques --;5 MOEA Comparisons and Theory --;5.1 MOEA Technique Comparisons --;5.2 MOEA Theory and Reviews --;6 Alternative Multiobjective Techniques --;Appendix B: MOPs in the Literature --;Appendix E: Moea Software Availability --;1 Introduction --;Appendix F: Moea-Related Information --;1 Introduction --;2 Websites of Interest --;3 Conferences --;4 Journals --;5 Researchers --;6 Distribution Lists --;References.
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| Abstract
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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. For additional information and supplementary teaching materials, please visit the authors' website at http://www.cs.cinvestav.mx/~EVOCINV/bookinfo.html.
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Artificial intelligence.
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Computer science.
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Information theory.
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| Added Entry
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Carlos A Coello Coello
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David A Veldhuizen
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Gary B Lamont
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