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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hong Li Yin Yong Ming Wang |
| Copyright Year | 2012 |
| Abstract | Genetic algorithms have demonstrated considerable success in providing efficient solutions to many non-polynomial-hard optimization problems. But unsuitable parameters may cause terrible solution for a specific scheduling problem. In this paper, we propose a genetic algorithm with parameters optimization mechanism, which can find the fittest control parameters, namely, number of population, probability of crossover, probability of mutation, for a given problem with a fraction of time, and then those parameters are used in the genetic algorithm for further more search operation to find optimal solution. For large scale problem, this novel genetic algorithm can get optimal control parameters effectively and get better solution, avoiding waste time caused by unfitted parameters. The algorithm is validated based on some benchmark problems of job shop scheduling. |
| Starting Page | 587 |
| Ending Page | 591 |
| File Size | 501577 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467348737 |
| DOI | 10.1109/CIT.2012.125 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Genetic algorithm (GA) Job shop scheduling Optimal computing budget allocation (OCBA) Approximation algorithms NP problems Approximation methods Optimization Genetic algorithms Testing Control parameters |
| Content Type | Text |
| Resource Type | Article |
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