Polynomial modeling for manufacturing processes using a backward elimination based genetic programming
Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on un...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/41355 |
| _version_ | 1848756122752647168 |
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| author | Chan, Kit Yan Dillon, T. Kwong, C. |
| author_facet | Chan, Kit Yan Dillon, T. Kwong, C. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods. © 2010 IEEE. |
| first_indexed | 2025-11-14T09:07:11Z |
| format | Conference Paper |
| id | curtin-20.500.11937-41355 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:07:11Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-413552017-09-13T14:09:29Z Polynomial modeling for manufacturing processes using a backward elimination based genetic programming Chan, Kit Yan Dillon, T. Kwong, C. Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods. © 2010 IEEE. 2010 Conference Paper http://hdl.handle.net/20.500.11937/41355 10.1109/CEC.2010.5586309 restricted |
| spellingShingle | Chan, Kit Yan Dillon, T. Kwong, C. Polynomial modeling for manufacturing processes using a backward elimination based genetic programming |
| title | Polynomial modeling for manufacturing processes using a backward elimination based genetic programming |
| title_full | Polynomial modeling for manufacturing processes using a backward elimination based genetic programming |
| title_fullStr | Polynomial modeling for manufacturing processes using a backward elimination based genetic programming |
| title_full_unstemmed | Polynomial modeling for manufacturing processes using a backward elimination based genetic programming |
| title_short | Polynomial modeling for manufacturing processes using a backward elimination based genetic programming |
| title_sort | polynomial modeling for manufacturing processes using a backward elimination based genetic programming |
| url | http://hdl.handle.net/20.500.11937/41355 |