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 6-10 of 86
... selection mechanism is used to evaluate the individuals of the population. When the search of GA proceeds, the population undergoes evolution with fitness, forming thus new a population. At that time, in each generation, relatively good ...
... Selection Selection provides the driving force in a GA. With too much force, a genetic search will be slower than necessary. Typically, a lower selection pressure is indicated atthe start of a genetic search in favor of a wide ...
... selecting a single chromosome for the new procedure. In contrast with proportional selection, (μ +λ)-selection are deterministic procedures that select the best chromosomes from parents and offspring. Note that both methods prohibit ...
... selection evaluation Solution candidates fitness computation t←t+1 crossover mutation CC(t) CM(t) hill-climbing CL(t) decoding decoding Kennedy gave an explanation of hGA with Lamarckian evolution theory. Fig. 1.5 The general structure ...
... selection. In the nineteenth century, Darwin's theory was challenged by Lamarck, who proposed that environmental changes throughout an organism's life cause structural changes that are transmitted to offspring. This theory lets ...
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 |