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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... average, intermediate, extended intermediate crossover, are usually adopted. The direction-based operators are formed by introducing the approximate gradient direction into genetic operators. The direction-based crossover operator uses ...
... average fitness which is occurred at each generation. By using this basic heuristic updating strategy, they can build up a detailed scheme for its implementation. For the detailed scheme, they use the changes of the average fitness ...
... and exploration based on change of the average fitness of the current and last generations . Different to conventional auto-tuning strategy, this auto-tuning based GA (atGA) comprises. 22 1 Multiobjective Genetic Algorithms.
... average fitness which occur in parents and offspring populations during continuous u generations of GA: it increases the occurrence probability of pM and decreases the occurrence probability of pC and pI if it consistently produces ...
... average on more than one objective. Fig. 1.14 Illustration of veGA selection A simple two objective problem with one variable is used 1.4 Multiobjective Genetic Algorithms 31. chromosome performance 12 . . . q generation t parents ...
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 |