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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... roulette wheel selection terminating condition? Y N New Population best solution Fig. 1.1 The general structure of genetic algorithms Figure 1.1 shows a general structure of GA. Let P(t) andC(t) be parents and offspring in current ...
... Selection Selection provides the driving force in a GA. With too much force, a genetic search will be slower than ... Roulette wheel selection • (μ +λ)-selection • Tournament selection • Truncation selection • Elitist selection • Ranking ...
... 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 procedure. In contrast with ...
... roulette wheel P(t)+C(t)+CL(t) selection evaluation Solution candidates fitness computation t←t+1 crossover mutation CC(t) CM(t) hill-climbing CL(t) decoding decoding Kennedy gave an explanation of hGA with Lamarckian evolution theory.
... roulette wheel selection routine; t ← t +1; end output Pareto optimal solutions E(P,C) end Procedure OX Input: two parents Output:offspring Step1: Step 2: Step 60 2 Basic Network Models. min z1 = n ∑ i=1 n ∑ j=1 cijxij (2.4) n n min ...
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 |