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 40
Page 186
2 30 1 5 2 3 0 5 2 1 double string ringed double string Figure 4.15 . Double string and ringed double string . Generally , a solution obtained in the direct way may not be feasible , because if we take all values specified by a give ...
2 30 1 5 2 3 0 5 2 1 double string ringed double string Figure 4.15 . Double string and ringed double string . Generally , a solution obtained in the direct way may not be feasible , because if we take all values specified by a give ...
Page 188
Example of PMX for double strings . An example is illustrated in Figure 4.16 . Note that the probability of the middle part of a string being exchanged is higher than for other parts of the string . Ringed double strings , where every ...
Example of PMX for double strings . An example is illustrated in Figure 4.16 . Note that the probability of the middle part of a string being exchanged is higher than for other parts of the string . Ringed double strings , where every ...
Page 237
known one- or two - cutpoint crossover methods , which work well with bit string encodings . For many complex problems , it is usually very difficult to represent a solution using bit string encodings . Therefore , many non - bit string ...
known one- or two - cutpoint crossover methods , which work well with bit string encodings . For many complex problems , it is usually very difficult to represent a solution using bit string encodings . Therefore , many non - bit string ...
What people are saying - Write a review
We haven't found any reviews in the usual places.
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
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 |