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 82
... Parameter values that genetic algorithm uses (population size, probabilities of applying genetic operators, etc.). Initial ... parameters output: the best solution. 2 1 Multiobjective Genetic Algorithms GeneralStructureofaGeneticAlgorithm.
... parameters output: the best solution begin t ← 0; initialize P(t) by encoding routine; evaluate P(t) by decoding routine; while (not terminating condition) do create C(t) fromP(t) by crossover routine; create C(t) from P(t) by mutation ...
... parameters, contains variances and covariances of the normal distribution for mutation. The purpose for incorporating the strategy parameters into the representation of individuals is to facilitate the evolutionary self-adaptation of ...
... parameters adaptation. The behaviors of GA are characterized by the balance between exploitation and exploration in the search space. The balance is strongly affected by the strategy parameters such as population size, maximum ...
... Parameter. Adaptation. Since GA are inspired from the idea of evolution, it is natural to expect thatthe adaptation is ... parameters of the changing configurations of GA while solving the problem. According to Herrera. Fig. 1.6 Applying a ...
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 |