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 75
... space Encoding space illegal feasible infeasible The infeasibility of chromosomes originates from the nature of the constrained optimization problem. Whatever methods are used, conventional ones or genetic algorithms, they must handle ...
... space Encoding space 1-to-1 mapping 1-to-n mapping n-to-1 mapping 1.2.2.4 Initialization In general, there are two ways to generate the initial population, i.e., the heuristic initialization and random initialization while satisfying ...
... space. In general, the exploitation of the accumulated information resulting from GA search is done by the selection mechanism, while the exploration to new regions of the search space is accounted for by genetic operators. The genetic ...
... space, and reduces the chances of settling for a false optimum; but if this probability is too high, it results in the wastage of a lot of computation time in exploring unpromising regions of the solution space. Up to now, several ...
... space, while a higher selection pressure is recommended at the end to narrow the search space. The selection directs the genetic search toward promising regions in the search space. During the past two decades, many selection methods ...
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 |