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Genetic Algorithms and Fuzzy Multiobjective Optimization

  • Book
  • © 2002

Overview

Authors:
  1. Masatoshi Sakawa

    1. Department of Artificial Complex Systems Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan

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Part of the book series: Operations Research/Computer Science Interfaces Series (ORCS, volume 14)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Introduction

    • Masatoshi Sakawa
    Pages 1-10

  3. Foundations of Genetic Algorithms

    • Masatoshi Sakawa
    Pages 11-27

  4. Genetic Algorithms for 0–1 Programming

    • Masatoshi Sakawa
    Pages 29-52

  5. Fuzzy Multiobjective 0–1 Programming

    • Masatoshi Sakawa
    Pages 53-82

  6. Genetic Algorithms for Integer Programming

    • Masatoshi Sakawa
    Pages 83-105

  7. Fuzzy Multiobjective Integer Programming

    • Masatoshi Sakawa
    Pages 107-131

  8. Genetic Algorithms for Nonlinear Programming

    • Masatoshi Sakawa
    Pages 133-151

  9. Fuzzy Multiobjective Nonlinear Programming

    • Masatoshi Sakawa
    Pages 153-168

  10. Genetic Algorithms for Job-Shop Scheduling

    • Masatoshi Sakawa
    Pages 169-188

  11. Fuzzy Multiobjective Job-Shop Scheduling

    • Masatoshi Sakawa
    Pages 189-222

  12. Some Applications

    • Masatoshi Sakawa
    Pages 223-272

  13. Back Matter

    Pages 273-288
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Keywords

About this book

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.

Authors and Affiliations

  • Department of Artificial Complex Systems Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan

    Masatoshi Sakawa

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