Network Models and Optimization: Multiobjective Genetic Algorithm ApproachNetwork models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems. |
From inside the book
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... considered. First, a genetic representation of solutions should be decided (i.e., encoding); second, a fitness function for evaluating solutions should be given. (i.e., decoding); third, genetic operators such as crossover operator ...
... considered as important factors, in order to keep a balance between exploration and exploitation in search space. In general, the exploitation of the accumulated information resulting from GA search is done by the selection mechanism ...
... considered as the input variables of GA and are also taken as the output variables of the FLC. For successfully applying an FLC to a GA in [41, 42], the key is to produce well-formed fuzzy sets and rules. Recently, Cheong and Lai ...
... considered as a repetitive process of nt+1 selection operations, with pj being the probability of selection of the j−th individual. For most of the selection operators, such as fitness proportionate selection and tournament selection ...
... considered that are already used in different moGA studies. They provide a good estimate of convergence if a reference set for S* (i.e., the Pareto optimal solution set or a near Pareto optimal solution set) is chosen as shown in Fig ...
Contents
1 | |
49 | |
Logistics Network Models | 135 |
Communication Network Models | 229 |
Advanced Planning and Scheduling Models | 297 |
Project Scheduling Models | 419 |
Assembly Line Balancing Models | 477 |
Tasks Scheduling Models | 551 |
References | 604 |
Index | 687 |
Other editions - View all
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2008 |
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2010 |