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 88
... Fitness. Evaluation. Fitness evaluation is to check the solution value of the objective function subject to the problem constraints. In general, the objective function provides the mechanism evaluating each individual. However, its range ...
... fitness value. Then a model roulette wheel can be made displaying these probabilities. The selection process is based on spinning the wheel the number of times equal to population size, each selecting a single chromosome for the new ...
... fitness is changed and the genotype remains unchanged. According to Whitley, Gordon and Mathias' experiences on some test problems, the Baldwinian search strategy can sometimes converge to a global optimum when the Lamarckian strategy ...
... fitness fitness Local optimum Local optimum improve improve Global optimum Global optimum ove ove fitness fitness impr Local optimum impr Local optimum Search range Search range for local search for local search Solution by GA Solution ...
... fitness function Among these classes, parameter adaptation has been extensively studied in the past ten years because the strategy parameters such as mutation probability, crossover probability, and population size are key factors in ...
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 |