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 87
... simple genetic operators are designed as general purpose search methods (the domain-independent search methods) they perform essentially a blind search and could not guarantee to yield an improved offspring. 1.1.3 Population-based ...
... simple one-cut-point crossover operation. Because an illegal chromosome cannot be decoded to a solution, the penalty techniques are inapplicable to this situation. Repairing techniques are usually adopted to convert an illegal ...
... simple way to achieve crossover would be to choose a random cut-point and generate the offspring by combining the segment of one parent to the left of the cut-point with the segment of the other parent to the right of the cut-point ...
... simple GA does not perform particularly well, and various methods of hybridization have beenproposed. One of most common forms of hybrid genetic algorithm (hGA) is to incorporate local optimization as an add-on extra to the canonical GA ...
... simple GA, which is Lamarkian in nature [19]. 1100101010 1011101110 . . . 0011011001 1100110001 1100101010 1011101110 1100101110 1011101010 0011011001 0011001001 Population P(t) chromosome Offspring C(t) chromosome 0001000001 roulette ...
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 |