Genetic Algorithms and Fuzzy Multiobjective Optimization - Masatoshi Sakawa

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Résumé :
Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness. The intended readers of this book are senior undergraduate students, graduate students, researchers, and practitioners in the fields of operations research, computer science, industrial engineering, management science, systems engineering, and other engineering disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real world research applications are used throughout the book to illustrate the presentation. These applications are drawn from complex problems. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.

Biographie:
Masatoshi Sakawa was born in Matsuyama, Japan on 11 August 1947. He received B.E., M.E., and D.E. degrees in applied mathematics and physics at Kyoto University in 1970, 1972, and 1975, respectively. From 1975 he was with Kobe University where, since 1981, he was an Associate Professor in the Department of Systems Engineering. From 1987 to 1990 he was a Professor in the Department of Computer Science at Iwate University. At present he is a Professor at Hiroshima University and is working with the Department of Artificial Complex Systems Engineering in the Graduate School of Engineering. He was an Honorary Visiting Professor at University of Manchester Institute of Science and Technology (UMIST), Computation Department, sponsored by the Japan Society for the Promotion of Science (JSPS) from March to December 1991. He was also a Visiting Professor at the Kyoto Institute of Economic Research, Kyoto University from April 1991 to March 1992. His research and teaching activities are in the area of systems engineering, especially mathematical optimization, multiobjective decision making, fuzzy mathematical programming and game theory. In addition to over 300 articles in National and International Journals, he is an author and coauthor of 5 books in English and 14 books in Japanese, including the Springer titles Genetic Algorithms and Fuzzy Multiobjective Optimization...

Sommaire:
1. Introduction.- 1.1 Introduction and historical remarks.- 1.2 Organization of the book.- 2. Foundations of Genetic Algorithms.- 2.1 Outline of genetic algorithms.- 2.2 Coding, fitness, and genetic operators.- 3. Genetic Algorithms for 0-1 Programming.- 3.1 Introduction.- 3.2 Multidimensional 0-1 knapsack problems.- 3.3 0-1 programming.- 3.4 Conclusion.- 4. Fuzzy Multiobjective 0-1 Programming.- 4.1 Introduction.- 4.2 Fuzzy multiobjective 0-1 programming.- 4.3 Fuzzy multiobjective 0-1 programming with fuzzy numbers.- 4.4 Conclusion.- 5. Genetic Algorithms for Integer Programming.- 5.1 Introduction.- 5.2 Multidimensional integer knapsack problems.- 5.3 Integer programming.- 5.4 Conclusion.- 6. Fuzzy Multiobjective Integer Programming.- 6.1 Introduction.- 6.2 Fuzzy multiobjective integer programming.- 6.3 Fuzzy multiobjective integer programming with fuzzy numbers.- 6.4 Conclusion.- 7. Genetic Algorithms for Nonlinear Programming.- 7.1 Introduction.- 7.2 Floating-point genetic algorithms.- 7.3 GENOCOP III.- 7.4 Revised GENOCOP III.- 7.5 Conclusion.- 8. Fuzzy Multiobjective Nonlinear Programming.- 8.1 Introduction.- 8.2 Multiobjective nonlinear programming.- 8.3 Multiobjective nonlinear programming problem with fuzzy numbers.- 8.4 Conclusion.- 9. Genetic Algorithms for Job-Shop Scheduling.- 9.1 Introduction.- 9.2 Job-shop scheduling.- 9.3 Genetic algorithms for job-shop scheduling.- 10.Fuzzy Multiobjective Job-Shop Scheduling.- 10.1 Introduction.- 10.2 Job-shop scheduling with fuzzy processing time and fuzzy due date.- 10.3 Multiobjective job-shop scheduling under fuzziness.- 11.Some Applications.- 11.1 Flexible scheduling in a machining center.- 11.2 Operation planning of district heating and cooling plants.- 11.3 Coal purchase planning in electric powerplants.- References.