Table of contents

  1. Front Matter
    Pages i-x
  2. Masatoshi Sakawa
    Pages 1-10
  3. Masatoshi Sakawa
    Pages 11-27
  4. Masatoshi Sakawa
    Pages 29-52
  5. Masatoshi Sakawa
    Pages 53-82
  6. Masatoshi Sakawa
    Pages 83-105
  7. Masatoshi Sakawa
    Pages 107-131
  8. Masatoshi Sakawa
    Pages 133-151
  9. Masatoshi Sakawa
    Pages 153-168
  10. Masatoshi Sakawa
    Pages 169-188
  11. Masatoshi Sakawa
    Pages 189-222
  12. Masatoshi Sakawa
    Pages 223-272
  13. Back Matter
    Pages 273-288

About this book

Introduction

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.

Keywords

addition algorithms linear optimization multi-objective optimization nonlinear optimization operations research optimization scheduling

Authors and affiliations

  • Masatoshi Sakawa
    • 1
  1. 1.Department of Artificial Complex Systems Engineering, Graduate School of EngineeringHiroshima UniversityHigashi-HiroshimaJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-1519-7
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5594-6
  • Online ISBN 978-1-4615-1519-7
  • Series Print ISSN 1387-666X
  • About this book