Genetic Algorithms and Fuzzy Multiobjective Optimi...

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Title: Genetic Algorithms and Fuzzy Multiobjective Optimization
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600 dpi scan | 332 Pages | 13.4 MB

Introduces the advances in the field of genetic algorithm optimization for 0-1

programming, integer programming, nonconvex programming, and job-shop scheduling

problems under multiobjectiveness.

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.

Links

http://linkgrabber.blogspot.com/2006/11/genetic-algorithms-and-fuzzy.html




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