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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... space. Random search is an example of a strategy which explores the search space, ignoring the exploitation of the promising regions of the search space. GA is a class of general purpose search methods combining elements of directed and ...
... space. The point is then improved along the deepest descending direction gradually through iterations as shown in Fig. 1.2. This point-to-point approach embraces the danger of failing in local optima. GA performs a multi-directional ...
... spaces. 2. Robustness: The use of evolution operators makes GA very effective in performing a global search (in probability), while most conventional heuristics usually perform a local search. It has been proved by many studies that GA ...
... space. For example, the pair 01111111111 and 10000000000 belong to neighboring points in the phenotype space (points of the minimal Euclidean distances) but have the maximum Hamming distance in the genotype space. To cross the Hamming ...
... spaces alternatively: the encoding space and the so- lution space, or in the other words, the genotype space and the phenotype space. The genetic operators work on the genotype space while evaluation and selection work on the phenotype ...
Contents
1 | |
Basic Network Models | 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 |