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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Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. Preface. Network design optimization is ... approaches. Recent advances in evolutionary algorithms (EAs) focus on how to solve such practical network optimization ...
... approach embraces the danger of failing in local optima. GA performs a multi-directional search by maintaining a population of potential solutions. The population-to-population approach is hopeful to make the search escape from local ...
... approach is confined within a feasible region. feasibility of chromosomes. Michalewicz et al. pointed out that often such systems are much more reliable than any other genetic algorithms based on the penalty ap- 1.2.5.4 Penalizing ...
Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. 1.3.1. Genetic. Local. Search. The idea of ... approaches use the metaphor that an individual learns (hillclimbs) during its lifetime (generation). In the Lamarckian ...
Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. to the multiple objective optimization ... approaches and suitable for different cases of multiobjective optimization problems, in order to understanding the ...
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 |