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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... fitness. To create the next generation, new chromosomes, called offspring, are formed by either merging two chromosomes from current generation using a crossover operator or modifying a chromosome using a mutation operator. A new ...
... fitness. 4. Genetic operators that alter the genetic composition of offspring (crossover, mutation, selection, etc.). 5. Parameter values that genetic algorithm uses (population size, probabilities of applying genetic operators, etc ...
... fitness solutions develop, the crossover operator provides exploration in the neighborhood of each of them. In other words, what kinds of searches (exploitation or exploration) a crossover performs would be determined by the environment ...
... -independent) no termination condition? termination condition? no yes stop yes (a) conventional method stop Global optimum Global optimum fitness fitness Local optimum Local optimum Population-based Search MajorAdvantages.
... fitness function for evaluating solutions should be given. (i.e., decoding); third, genetic operators such as crossover operator, mutation operator and selection methods should be designed; last, a necessary component for applying GA to ...
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 |