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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Contents
Multiobjective Optimization Problems | 97 |
Pareto Tournament Method | 122 |
Fuzzy Optimization Problems | 142 |
Reliability Design Problems | 194 |
Scheduling Problems | 235 |
14 | 271 |
1127218068 | 283 |
38 | 289 |
Advanced Transportation Problems | 297 |
Network Design and Routing | 341 |
Manufacturing Cell Design | 390 |
451 | |
491 | |
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Common terms and phrases
assigned b₁ beam search calculated cell chromosome constraints cost crossover operator decision maker defined denote design problems determine edges equation evaluation evolutionary example feasible fitness function fitness value follows fundamental runs gene genetic algorithm approach genetic algorithms genetic operators genetic search given go to step goal programming heuristic ideal point individuals initial population knapsack problem linear programming m₁ matrix max_gen maximal maximum membership function minimize minimum spanning tree multiobjective optimization mutation operator network reliability nondominated nonlinear objective function objective value obtained offspring optimal solution optimization problems parameters parent Pareto optimal Pareto solutions permutation pop_size preference priority procedure production programming problem proposed Prüfer number R₁ random number randomly real number representation resource roulette wheel selection routing scheduling problem selection sequence shown in Figure solve spanning tree problem Table techniques topological sort transportation problem variable vector x₁ zmin
Popular passages
Page 460 - A new representation and operators for genetic algorithms applied to grouping problems.
Page 470 - Back, and J. Heitkotter. The zero/one multiple knapsack problem and genetic algorithms. In E. Deaton, D. Oppenheim, J. Urban, and H. Berghel, editors, Proceedings of the 1994 ACM Symposium on Applied Computing, pages 188-193.