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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jin Zeng Qing-Sheng Ren |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Mathematics, Shanghai Jiao Tong University, 200240, China (Jin Zeng) || Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200240, China (Qing-Sheng Ren) |
| Abstract | In this paper, we investigate the usage of history information for estimation of distribution algorithm (EDA). In EDA, the distribution is estimated from a set of selected individuals and then the estimated distribution model is used to generate new individuals. It needs large population size to converge to the global optimum. A new algorithm, the high-order EDA, is proposed based on the idea of filter. By the usage of history information, it can converge to the global optimum with high probability even with small population size. Convergence properties are then discussed. We also show the application for constrained optimization problems. |
| Starting Page | 3616 |
| Ending Page | 3621 |
| File Size | 226021 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424437023 |
| DOI | 10.1109/ICMLC.2009.5212795 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electronic design automation and methodology Constraint optimization History Genetic algorithms Machine learning Cybernetics Mathematics Computer science Filters Optimization methods Estimation of distribution algorithm (EDA) Convergence |
| Content Type | Text |
| Resource Type | Article |
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