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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... generation is formed by selection, according to the fitness values, some of the parents and offspring, and rejecting 1 others so as to keep the population size constant. Fitter Multiobjective Genetic Algorithms Introduction.
... parents and offspring in current generation t, respectively and the general implementation structure of GA is described as follows: procedure:basicGA input: problem data, GA parameters output: the best solution. 2 1 Multiobjective ...
... parents. Property 8 (Locality): Asmallchange in chromosome should imply a small change in its corresponding solution. Solution space Encoding space 1-to-1 mapping 1-to-n mapping n-to-1 mapping 1.2.2.4 Initialization In general, there ...
... parent to the left of the cut-point with the segment of the other parent to the right of the cut-point. This method ... parents by random numbers with some distribution. 1.2.4.2 Mutation Mutation is a background operator which produces ...
... parents, and the algorithm will lose the ability to learn from the history of the search. Up to now, several mutation operators have been proposed for real numbers encoding, which can roughly be put into four classes as crossover can be ...
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 |