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
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... given problem. The second way is used in the evolution strategies by Rechen- berg [4] and Schwefel [5]. An individual consists of two parts: the first part is the solution to a given problem and the second part, called strategy ...
... given problem, while illegality refers to the phenomenon that a chromosome does not represent a solution to a given problem. Solution space Encoding space illegal feasible infeasible The infeasibility of chromosomes originates from the ...
... given randomly as a free direction to avoid the chromosomes jamming into a corner. If the chromosome is near the boundary, the mutation direction given by some criteria might point toward the close boundary, and then jamming could occur ...
... given problem, but also for tuning the GA to the particular problem. During the past few years, many adaptation techniques have been suggested and tested in order to obtain an effective implementation of the GA to real-world problems ...
... given by Herrera and Lozano [36], and Hinterding et al. [37]. According to these classifications of adaptation, there are three main categories. 1. Deterministic adaptation: Deterministic adaptation takes place if the value of 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 |