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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... performance than the binary or Gray encoding for function optimizations and constrained optimizations. Since the topological structure of the genotype space for the real number encoding method is identical to that of the phenotype space ...
... performance of the decoded solutions. The mapping from the genotype space to the phenotype space has a considerable influence on the performance of the GA. The most prominent problem associated with mapping is that some individuals ...
... performance of GA depends to a great extent, on the performance of the crossover operator used. The crossover probability (denoted by pC )isdefined as the probability of the number of offspring produced in each generation to the ...
... performance on a divers set of constrained combinatorial optimization problems, that the repair strategy did indeed surpass other strategies in both speed and performance [32, 33]. Repairing strategy depends on the existence of a ...
... performance [40]. 3. Self-adaptive adaptation: Self-adaptive adaptation enables strategy parameters to evolve along with the evolutionary process. The parameters are encoded onto the chromosomes of the individuals and undergo mutation ...
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 |