Development of genetic algorithm for optimization of yield models in oil palm production

ABSTRACT 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 a...

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Autores principales:	Hilal,Yousif Y., Ishak,Wan, Yahya,Azmi, Asha'ari,Zulfa H.
Lenguaje:	English
Publicado:	Instituto de Investigaciones Agropecuarias, INIA 2018
Materias:	Air pollution climatic change Elaeis guineensis Sabah Sarawak selection variables and sensitivity test
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000200228
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spelling	oai:scielo:S0718-583920180002002282018-08-16Development of genetic algorithm for optimization of yield models in oil palm productionHilal,Yousif Y.Ishak,WanYahya,AzmiAsha'ari,Zulfa H. Air pollution climatic change Elaeis guineensis Sabah Sarawak selection variables and sensitivity test ABSTRACT 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.info:eu-repo/semantics/openAccessInstituto de Investigaciones Agropecuarias, INIAChilean journal of agricultural research v.78 n.2 20182018-06-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000200228en10.4067/S0718-58392018000200228
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Air pollution climatic change Elaeis guineensis Sabah Sarawak selection variables and sensitivity test
spellingShingle	Air pollution climatic change Elaeis guineensis Sabah Sarawak selection variables and sensitivity test Hilal,Yousif Y. Ishak,Wan Yahya,Azmi Asha'ari,Zulfa H. Development of genetic algorithm for optimization of yield models in oil palm production
description	ABSTRACT 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.
author	Hilal,Yousif Y. Ishak,Wan Yahya,Azmi Asha'ari,Zulfa H.
author_facet	Hilal,Yousif Y. Ishak,Wan Yahya,Azmi Asha'ari,Zulfa H.
author_sort	Hilal,Yousif Y.
title	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_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_sort	development of genetic algorithm for optimization of yield models in oil palm production
publisher	Instituto de Investigaciones Agropecuarias, INIA
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000200228
work_keys_str_mv	AT hilalyousify developmentofgeneticalgorithmforoptimizationofyieldmodelsinoilpalmproduction AT ishakwan developmentofgeneticalgorithmforoptimizationofyieldmodelsinoilpalmproduction AT yahyaazmi developmentofgeneticalgorithmforoptimizationofyieldmodelsinoilpalmproduction AT ashaarizulfah developmentofgeneticalgorithmforoptimizationofyieldmodelsinoilpalmproduction
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Development of genetic algorithm for optimization of yield models in oil palm production

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