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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hong-Zhen Zheng Xiao-Dong Zhang Hao-Yan Guo |
| Copyright Year | 2009 |
| Abstract | It proposed an idea of using support vector machines (SVMs) to learn the efficient set of a multiple objective discrete optimization (MODO) problem. We conjecture that a surface generated by SVM could provide a good approximation of the efficient set. As the efficient set is learned at a single SVM implementation by using a group of seeds that symbolize efficient and dominated solutions. To be able to observe whether learning the efficient set via SVMs might have practical implications, we incorporate the SVM-induced efficient set into a GA as a fitness function. We implement our SVM-guided GA on the multiple objective knapsack and assignment problems. We observe that using SVM improves the performance of the GA compared to a benchmark distance based fitness function and may provide competitive results. Our approach is a general one and can be applied to any MODO problem with any number of objective functions. |
| Starting Page | 489 |
| Ending Page | 493 |
| File Size | 383413 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769537351 |
| DOI | 10.1109/FSKD.2009.15 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-14 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Statistical learning Efficient Set Educational institutions MODO SVM Support vector machines Computer science Space technology Support vector machine classification Machine learning Risk management Fuzzy systems Mathematical programming |
| Content Type | Text |
| Resource Type | Article |
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