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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... represents the optimum or suboptimal solution to the problem. 1.1.1. General. Structure. of. a. Genetic. Algorithm. In general, a GA has five basic components, as summarized by Michalewicz [12]: 1. A genetic representation of potential ...
... represents. At a particular locus, a gene may encode one of several different values of the particular characteristic it represents. The different values of a gene are called alleles. The correspondence of GA terms and optimization ...
... represent their so- lutions with the binary encoding. Various encoding methods have been created for particular problems in order to have an effective implementation of the GA. According to what kind of symbols is used as the alleles of ...
... represent a solution to a given problem. Solution space Encoding space illegal feasible infeasible The infeasibility ... represented as a system of equalities or inequalities. For such cases, penalty methods can be used to handle ...
... Representing. Networks. Fig. 2.2 AdigraphG 1 2 3 5 4 6 7 8 9 10 Definition 2.1. Edge Lists A directed graph (or ... represented by adjacency lists as follows: 1. Its number of nodes n = 10; 2. The adjacency lists: S1 = {2,3}, S2 = {4,5} ...
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 |