Genetic Algorithms and Engineering OptimizationA comprehensive guide to a powerful new analytical tool by two of its foremost innovators The past decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in everything from product design to scheduling and client/server networking. Aided by GAs, analysts and designers now routinely evolve solutions to complex combinatorial and multiobjective optimization problems with an ease and rapidity unthinkable withconventional methods. Despite the continued growth and refinement of this powerful analytical tool, there continues to be a lack of up-to-date guides to contemporary GA optimization principles and practices. Written by two of the world's leading experts in the field, this book fills that gap in the literature. Taking an intuitive approach, Mitsuo Gen and Runwei Cheng employ numerous illustrations and real-world examples to help readers gain a thorough understanding of basic GA concepts-including encoding, adaptation, and genetic optimizations-and to show how GAs can be used to solve an array of constrained, combinatorial, multiobjective, and fuzzy optimization problems. Focusing on problems commonly encountered in industry-especially in manufacturing-Professors Gen and Cheng provide in-depth coverage of advanced GA techniques for: * Reliability design * Manufacturing cell design * Scheduling * Advanced transportation problems * Network design and routing Genetic Algorithms and Engineering Optimization is an indispensable working resource for industrial engineers and designers, as well as systems analysts, operations researchers, and management scientists working in manufacturing and related industries. It also makes an excellent primary or supplementary text for advanced courses in industrial engineering, management science, operations research, computer science, and artificial intelligence. |
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Page 192
... obtained , but the de- cision maker was not satisfied with the solution . Then the decision maker considered that the membership function values μ , and μ , should be improved με рез upon sacrifice of the membership function value μ ...
... obtained , but the de- cision maker was not satisfied with the solution . Then the decision maker considered that the membership function values μ , and μ , should be improved με рез upon sacrifice of the membership function value μ ...
Page 337
... obtained solutions with moderate degree ( a = 0.5 ) obtained solutions with pessimistic degree ( a = 0.0 ) 50 50 50 100 150 O O D D 200 250 first objective value Figure 7.13 . Solutions obtained with three degrees of optimism ( σ ) in ...
... obtained solutions with moderate degree ( a = 0.5 ) obtained solutions with pessimistic degree ( a = 0.0 ) 50 50 50 100 150 O O D D 200 250 first objective value Figure 7.13 . Solutions obtained with three degrees of optimism ( σ ) in ...
Page 340
... obtained solutions with optimistic degree ( a = 1.0 ) obtained solutions with moderate degree ( a = 0.5 ) A obtained solutions with pessimistic degree ( a = 0.0 ) Δ DIOBOD ΔΔ 150 150 250 350 450 550 650 750 first objective value Figure ...
... obtained solutions with optimistic degree ( a = 1.0 ) obtained solutions with moderate degree ( a = 0.5 ) A obtained solutions with pessimistic degree ( a = 0.0 ) Δ DIOBOD ΔΔ 150 150 250 350 450 550 650 750 first objective value Figure ...
Contents
Combinatorial Optimization Problems | 53 |
Multiobjective Optimization Problems | 97 |
Fuzzy Optimization Problems | 142 |
Copyright | |
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Common terms and phrases
adaptive applied assigned b₁ beam search calculated cell design chromosome combinatorial optimization computation constraints cost crossover operator decision maker defined denote design problem determine edge evaluation evolutionary example feasible fitness function fitness value follows gene genetic algorithm approach genetic algorithms genetic operators genetic search given go to step graph heuristic ideal point individuals initial population integer programming knapsack problem linear programming m-GA M₁ matrix max_gen membership function minimize minimum spanning tree multiobjective optimization mutation operator node nondominated nonlinear number of machines objective function objective value obtained offspring optimization problems P₁ packet parameters parent Pareto optimal Pareto solutions path permutation pop_size preference procedure process plan production programming problem proposed Prüfer number random number randomly representation roulette wheel selection scheduling problem selection sequence shown in Figure solve spanning tree problem st-GA strategy string techniques topological sort transportation problem variables weight