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 45
... initial set of random solutions called population satisfying boundary and/or system constraints to the problem. Each individual in the population is called a chromosome (or individual), representing a solution to the problem at hand ...
... initial set of potential solutions). 3. An evaluation function rating solutions in terms of their fitness. 4. Genetic operators that alter the genetic composition of offspring (crossover, mutation, selection, etc.). 5. Parameter values ...
... initial point initial single point initial point ... ... improvement (problem-specific) initial point improvement (problem-independent) no termination condition? termination condition? no yes stop yes (a) conventional method stop Global ...
... initial population, i.e., the heuristic initialization and random initialization while satisfying the boundary and/or system constraints to the problem. Although the mean fitness of the. Fig. 1.4 Mapping from chromosomes to solutions Fig ...
... initial population. 1.2.3. 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 ...
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 |