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 96
They give out both the ideal points and all Pareto optimal solutions . They clearly show that all Pareto optimal solutions are close to the ideal point under the action of the adaptive evaluation function . In the case of 50 vertices ...
They give out both the ideal points and all Pareto optimal solutions . They clearly show that all Pareto optimal solutions are close to the ideal point under the action of the adaptive evaluation function . In the case of 50 vertices ...
Page 109
The compromise approach identifies solutions closest to the ideal solution as determined by some measure of distance . As we know , the ideal solution is usually not attainable . However , it can serve as a standard for evaluation of ...
The compromise approach identifies solutions closest to the ideal solution as determined by some measure of distance . As we know , the ideal solution is usually not attainable . However , it can serve as a standard for evaluation of ...
Page 138
is the positive ideal point in the criterion space 2 and weights ( w ,, W2 , ... , w . ) are assigned to objectives to emphasize different degrees of importance . As we know , the ideal solution is not usually attainable .
is the positive ideal point in the criterion space 2 and weights ( w ,, W2 , ... , w . ) are assigned to objectives to emphasize different degrees of importance . As we know , the ideal solution is not usually attainable .
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Contents
Combinatorial Optimization Problems | 53 |
Multiobjective Optimization Problems | 97 |
Fuzzy Optimization Problems | 142 |
Copyright | |
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Common terms and phrases
according activity adaptive alternative applied approach assigned calculated cell chromosome combination combinatorial optimization complete computation connected considered constraints corresponding cost decision maker defined demand denote determine direction edge encoding equal evaluation evolutionary example feasible Figure fitness fitness value fixed follows fuzzy gene genetic algorithms genetic operators given heuristic ideal individuals infeasible initial integer linear machine matrix maximum means membership method minimize mutation node objective function obtained offspring operator optimal solution optimization problems otherwise parameters parent Pareto solutions path performance permutation population positive possible preference probability problem procedure production programming proposed random randomly reliability representation represented resource roulette wheel selection route runs schedule selection sequence shown in Figure solve space Step strategy string Table techniques transportation usually variables weight
Bibliographic information
| Title | Genetic Algorithms and Engineering Optimization A Wiley-Interscience publication Volume 7 of Engineering Design and Automation |
| Authors | Mitsuo Gen, Runwei Cheng, Gen |
| Edition | illustrated, reprint |
| Publisher | Wiley, 2000 |
| Original from | the University of Michigan |
| Digitized | 27 Nov 2007 |
| ISBN | 0471315311, 9780471315315 |
| Length | 495 pages |
| Subjects | › › Computers / Artificial Intelligence / General Technology & Engineering / General Technology & Engineering / Industrial Engineering Technology & Engineering / Mechanical |
| Export Citation | BiBTeX EndNote RefMan |