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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... Encoded solution. 1.2.2. Encoding. Issue. How to encode a solution of a given problem into a chromosome is a key issue for the GA. This issue has been investigated from many aspects, such as mapping characters from a genotype space to a ...
... encoding is suitable for combinatorial optimization problems. Since the essence of combinatorial optimization problems is to search for a best permutation or combination of some items subject to some constraints, the literal permutation ...
... encoding techniques. For many combinatorial optimization problems, problem-specific encodings are used and such encodings usually yield illegal offspring by a simple one-cut-point crossover operation. Because an illegal chromosome ...
... Encodings Given a new encoding method, it is usually necessary to examine whether we can build an effective genetic search with the encoding. Several principles have been proposed to evaluate an encoding [15, 25]: Property 1 (Space): ...
... encoding, which can roughly be put into four classes as crossover can be classified. Random mutation operators such as uniform mutation, boundary mutation, and plain mutation, belong to the conventional mutation operators, which simply ...
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 |