Genetic Algorithms and Fuzzy Multiobjective Optimization
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.
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FOUNDATIONS OF GENETIC ALGORITHMS
GENETIC ALGORITHMS FOR 01 PROGRAMMING 29
FUZZY MULTIOBJECTIVE 01 PROGRAMMING
GENETIC ALGORITHMS FOR INTEGER PROGRAMMING
FUZZY MULTIOBJECTIVE INTEGER PROGRAMMING
GENETIC ALGORITHMS FOR NONLINEAR
FUZZY MULTIOBJECTIVE NONLINEAR PROGRAMMING
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0-1 knapsack problems 0-1 programming problems a-level augmented minimax problem bisection method branch and bound calculated cmult constraints crossover decoding algorithm degrees of similarity denote double strings expected value selection feasible region Figure fitness function FJSP formulated fuzzy completion fuzzy due date fuzzy goals fuzzy numbers fuzzy processing fuzzy satisficing method GADS GADSLPR GADSLPRRSU GADSRSU genetic algorithms genetic operators Giffler and Thompson go to step Imax Imin individual minimum initial population integer programming problem interactive fuzzy satisficing job-shop scheduling problems linear membership functions linear programming LP_SOLVE machine membership function values minimize multidimensional 0-1 knapsack multiobjective integer programming mutation nonlinear programming numerical example numerical experiments objective functions offspring Pareto optimal solution phenotype problems with fuzzy random number randomly reference membership levels reference membership values reference solution updating return to step revised GENOCOP Sakawa satisficing solution satisfied search point shown in Table simulated annealing solved Thompson algorithm trials vector