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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... illustrated by the evolution of our society. Ideas and knowledge are passed from generation to generation through structured language and culture. GA, the ar- tificial organisms, can benefit from the advantages of Lamarckian theory. By ...
... illustrated in Fig. 1.14, protects the survival of the best individuals on one of the objectives and, simultaneously ... Illustration of veGA selection A simple two objective problem with one variable is used 1.4 Multiobjective Genetic ...
... illustrated in Fig. 1.17 for a simple case with two objectives to be simultaneously minimized. Multiobjective Genetic Algorithm ... Illustration of Goldberg's ranking (minimization case) Fig. 1.18. 1.4 Multiobjective Genetic Algorithms 33.
... Illustration of Goldberg's ranking (minimization case) Fig. 1.18 Illustration of moGA ranking (minimization case) dummy fitness value. These solutions are shared with their dummy fitness values (phenotypic sharing on the decision ...
... Illustration of fixed direction search and multiple direction search in criterion space (minimization case) 1 :rwGA procedure : the objective () of eachchromosome , 1,2,...,, ki i f v v k q i popSize input : fitness value (), i evalv i ...
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 |