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
Results 1-5 of 84
... first way is widely adopted to conceive a suitable encoding to a given problem. The second way is used in the evolution strategies by Rechen- berg [4] and Schwefel [5]. An individual consists of two parts: the first part is the solution ...
... first advocates modifying some components of GA, such as representation, crossover, mutation and selection, in order to choose an appropriate form of the algorithm to meet the nature of a given problem. The second suggests a way to tune ...
... first notable work to solve the multiobjective problems. Instead of using a scalar fitness measure to evaluate each chromosome, it uses a vector fitness measure to create the next generation. In the veGA approach, the selection step in ...
... first effort of opening the domain of multiple objective optimizations to the GA. Even though veGA cannot give a satisfactory solution to the multiple objective optimization problem, it provides some useful hints for developing other ...
... is a two-stage process. First, the individuals in the external nondominated set P are ranked. Each solution i ∈ P is assigned a real value si ∈ [0,1), called strength; si is 1.4 Multiobjective Genetic Algorithms 35.
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 |