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. |
From inside the book
Results 1-3 of 82
Page 117
... Figure 3.5 plots the problem in the criterion space . In the simulation results of VEGA given by Srinivas and Deb ... Figure 3.6 . In generation 10 , the population converged toward the nondominated region , as shown in Figure 3.7 . In ...
... Figure 3.5 plots the problem in the criterion space . In the simulation results of VEGA given by Srinivas and Deb ... Figure 3.6 . In generation 10 , the population converged toward the nondominated region , as shown in Figure 3.7 . In ...
Page 392
... [ Figure 9.1 ( c ) ] . The machines are generally arranged in a linear or U - shaped layout . Therefore , a family of ... Figure 9.2 ( a ) . The ( i , k ) th element of the matrix is 1 if the jth part is processed by the kth machine ...
... [ Figure 9.1 ( c ) ] . The machines are generally arranged in a linear or U - shaped layout . Therefore , a family of ... Figure 9.2 ( a ) . The ( i , k ) th element of the matrix is 1 if the jth part is processed by the kth machine ...
Page 424
... Figure 9.10 . Original matrix without machine redundancy . on average for 10 replications to solve the problem . The solution , having a I of 0.7308 and 10 exceptional elements , is shown in Figure 9.11 . Further evaluation using the ...
... Figure 9.10 . Original matrix without machine redundancy . on average for 10 replications to solve the problem . The solution , having a I of 0.7308 and 10 exceptional elements , is shown in Figure 9.11 . Further evaluation using the ...
Contents
Combinatorial Optimization Problems | 53 |
Multiobjective Optimization Problems | 97 |
Fuzzy Optimization Problems | 142 |
Copyright | |
7 other sections not shown
Other editions - View all
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