Development of genetic algorithm for optimization of yield models in oil palm production
For many years the Malaysian oil palm (Elaeis guineensis Jacq.) industry has been facing the challenge of the reduced rate of palm oil yield caused by the gap in the oil palm production and high land usage. In the oil palm industry, modelling and selecting variables play a crucial role in apprehendi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Instituto de Investigaciones Agropecuarias (INIA)
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/72259/ http://psasir.upm.edu.my/id/eprint/72259/1/Development%20of%20genetic%20algorithm%20for%20optimization%20of%20yield%20models%20in%20oil%20palm%20production.pdf |
| _version_ | 1848857075712524288 |
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| author | Hilal, Yousif Y. Wan Ismail, Wan Ishak Yahya, Azmi Ash’aari, Zulfa Hanan |
| author_facet | Hilal, Yousif Y. Wan Ismail, Wan Ishak Yahya, Azmi Ash’aari, Zulfa Hanan |
| author_sort | Hilal, Yousif Y. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | For many years the Malaysian oil palm (Elaeis guineensis Jacq.) industry has been facing the challenge of the reduced rate of palm oil yield caused by the gap in the oil palm production and high land usage. In the oil palm industry, modelling and selecting variables play a crucial role in apprehending different issues, i.e. decision making. Nonetheless, the advance in computer technology has created a new opportunity for the study of modelling as selecting variables intended to choose the “best” subset of predictors. Owing to this great interest in the predictions, the study aims to develop a genetic algorithm (GA) to identify the relevant variables and search for the best combinations for modelling to examine the potential of oil palm production in Sarawak and Sabah, Borneo, Malaysia, under a given set of assumptions. Eleven years of high climatic change and air pollution are utilized to secure findings where the primary variable, i.e. the evaporation and surface wind speed, were recorded on the proportion of effect reached up to 100% in Sarawak and Sabah, respectively. Moreover, models were built on the basis of variables that have been selected by the GA. Across the optimization, procedures obtained the best Two Factor Interaction (2FI) models to achieve the best model of oil palm productivity prediction with a value of R2 of 0.948, mean squared error of 0.022, and the model P-value of < 0.0001 in Sabah. This research concludes that the GA method is a user-friendly variable selection tool with excellent results because it can choose variables correctly. |
| first_indexed | 2025-11-15T11:51:47Z |
| format | Article |
| id | upm-72259 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:51:47Z |
| publishDate | 2018 |
| publisher | Instituto de Investigaciones Agropecuarias (INIA) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-722592020-04-26T16:26:14Z http://psasir.upm.edu.my/id/eprint/72259/ Development of genetic algorithm for optimization of yield models in oil palm production Hilal, Yousif Y. Wan Ismail, Wan Ishak Yahya, Azmi Ash’aari, Zulfa Hanan For many years the Malaysian oil palm (Elaeis guineensis Jacq.) industry has been facing the challenge of the reduced rate of palm oil yield caused by the gap in the oil palm production and high land usage. In the oil palm industry, modelling and selecting variables play a crucial role in apprehending different issues, i.e. decision making. Nonetheless, the advance in computer technology has created a new opportunity for the study of modelling as selecting variables intended to choose the “best” subset of predictors. Owing to this great interest in the predictions, the study aims to develop a genetic algorithm (GA) to identify the relevant variables and search for the best combinations for modelling to examine the potential of oil palm production in Sarawak and Sabah, Borneo, Malaysia, under a given set of assumptions. Eleven years of high climatic change and air pollution are utilized to secure findings where the primary variable, i.e. the evaporation and surface wind speed, were recorded on the proportion of effect reached up to 100% in Sarawak and Sabah, respectively. Moreover, models were built on the basis of variables that have been selected by the GA. Across the optimization, procedures obtained the best Two Factor Interaction (2FI) models to achieve the best model of oil palm productivity prediction with a value of R2 of 0.948, mean squared error of 0.022, and the model P-value of < 0.0001 in Sabah. This research concludes that the GA method is a user-friendly variable selection tool with excellent results because it can choose variables correctly. Instituto de Investigaciones Agropecuarias (INIA) 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72259/1/Development%20of%20genetic%20algorithm%20for%20optimization%20of%20yield%20models%20in%20oil%20palm%20production.pdf Hilal, Yousif Y. and Wan Ismail, Wan Ishak and Yahya, Azmi and Ash’aari, Zulfa Hanan (2018) Development of genetic algorithm for optimization of yield models in oil palm production. Chilean Journal of Agricultural Research, 78 (2). 228 - 237. ISSN 0718-5820 ; ESSN: 0718-5839 http://www.chileanjar.cl/m/abstractms.php?cmd=ABSTRACT&lang=ENG&id=4580 10.4067/S0718-58392018000200238 |
| spellingShingle | Hilal, Yousif Y. Wan Ismail, Wan Ishak Yahya, Azmi Ash’aari, Zulfa Hanan Development of genetic algorithm for optimization of yield models in oil palm production |
| title | Development of genetic algorithm for optimization of yield models in oil palm production |
| title_full | Development of genetic algorithm for optimization of yield models in oil palm production |
| title_fullStr | Development of genetic algorithm for optimization of yield models in oil palm production |
| title_full_unstemmed | Development of genetic algorithm for optimization of yield models in oil palm production |
| title_short | Development of genetic algorithm for optimization of yield models in oil palm production |
| title_sort | development of genetic algorithm for optimization of yield models in oil palm production |
| url | http://psasir.upm.edu.my/id/eprint/72259/ http://psasir.upm.edu.my/id/eprint/72259/ http://psasir.upm.edu.my/id/eprint/72259/ http://psasir.upm.edu.my/id/eprint/72259/1/Development%20of%20genetic%20algorithm%20for%20optimization%20of%20yield%20models%20in%20oil%20palm%20production.pdf |