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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Sangwan, Kuldip Singh | - |
| dc.date.accessioned | 2023-08-31T07:15:49Z | - |
| dc.date.available | 2023-08-31T07:15:49Z | - |
| dc.date.issued | 2015-07 | - |
| dc.identifier.uri | https://www.emerald.com/insight/content/doi/10.1108/EC-03-2014-0047/full/html | - |
| dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/11756 | - |
| dc.description.abstract | The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that there are many models in the population whose performance is better than that of the model selected with a little compromise on training error. Therefore, an ensemble evolutionary (Ensemble-MGGP) approach is proposed and applied to the data obtained from the vibratory finishing process. The paper aims to discuss these issues. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Emerald | en_US |
| dc.subject | Mechanical Engineering | en_US |
| dc.subject | Surface finish prediction | en_US |
| dc.subject | Vibratory finishing process modelling | en_US |
| dc.subject | Vibratory modelling | en_US |
| dc.title | An ensemble evolutionary approach in evaluation of surface finish reduction of vibratory finishing process | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Department of Mechanical engineering | |
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