Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development
The application of data engineering techniques like a genetic algorithm in forecasting outcomes in plant genetics and breeding can help solve the twin problems of food insecurity and insufficiency. To demonstrate the practicality of using artificial intelligence (AI) to address these problems, t...
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my-inti-eprints.10622023-11-15T05:39:56Z http://eprints.intimal.edu.my/1062/ Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development Kehinde, Okewu Emmanuel, Okewu Wong, Ling Shing Siti Sarah, Maidin QA Mathematics QA76 Computer software QH426 Genetics The application of data engineering techniques like a genetic algorithm in forecasting outcomes in plant genetics and breeding can help solve the twin problems of food insecurity and insufficiency. To demonstrate the practicality of using artificial intelligence (AI) to address these problems, the genetic algorithm is applied to genetic engineering (genetic mutation) of cowpea in a crop improvement program to generate useful bioinformatic information for further improvement of the crop. The aim of this work is to address malnutrition, immune deficiency, hunger, and poverty as canvassed in United Nations Sustainable Development Goals 1 and 2 (SDGs 1 and 2). Three genotypes (specifies) of cowpea obtained from Kontagora in Niger State of Nigeria were treated with chemical and physical mutagens: 200, 400, 600, and 800 of ethyl methane sulphonate (EMS) and 0.372gy of gamma rays. The study applied genetic algorithm as a stochastic optimizer using Python programming to determine the convergence pattern for obtaining an optimal cowpea solution that combines high yield and drought-tolerance. Huge data was generated in three iterative experiments. The outcomes of the three experiments showed that in experiment 1, the convergence occurred in the 9412th generation while in experiment 2, we obtained convergence in the 899th generation of the cowpea. Experiments show that the genetic mutation resulted in phenotypic traits in the first-generation offspring. The result of the third experiment indicated that the optimal cowpea solution was obtained in the 14338th generation. This implies that the use of AI (genetic algorithm) in ensuring food security and sufficiency may be time-consuming but would result in the desired traits in crops for meeting the 4 pillars of sustainability (human, social, economic and environmental). INTI International University 2023-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1062/1/jods2023_12.pdf Kehinde, Okewu and Emmanuel, Okewu and Wong, Ling Shing and Siti Sarah, Maidin (2023) Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development. Journal of Data Science, 2023 (12). pp. 1-13. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
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QA Mathematics QA76 Computer software QH426 Genetics Kehinde, Okewu Emmanuel, Okewu Wong, Ling Shing Siti Sarah, Maidin Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development |
description |
The application of data engineering techniques like a genetic algorithm in forecasting outcomes in
plant genetics and breeding can help solve the twin problems of food insecurity and insufficiency.
To demonstrate the practicality of using artificial intelligence (AI) to address these problems, the
genetic algorithm is applied to genetic engineering (genetic mutation) of cowpea in a crop
improvement program to generate useful bioinformatic information for further improvement of the
crop. The aim of this work is to address malnutrition, immune deficiency, hunger, and poverty as
canvassed in United Nations Sustainable Development Goals 1 and 2 (SDGs 1 and 2). Three
genotypes (specifies) of cowpea obtained from Kontagora in Niger State of Nigeria were treated
with chemical and physical mutagens: 200, 400, 600, and 800 of ethyl methane sulphonate (EMS)
and 0.372gy of gamma rays. The study applied genetic algorithm as a stochastic optimizer using
Python programming to determine the convergence pattern for obtaining an optimal cowpea
solution that combines high yield and drought-tolerance. Huge data was generated in three iterative
experiments. The outcomes of the three experiments showed that in experiment 1, the convergence
occurred in the 9412th generation while in experiment 2, we obtained convergence in the 899th
generation of the cowpea. Experiments show that the genetic mutation resulted in phenotypic traits
in the first-generation offspring. The result of the third experiment indicated that the optimal
cowpea solution was obtained in the 14338th generation. This implies that the use of AI (genetic
algorithm) in ensuring food security and sufficiency may be time-consuming but would result in
the desired traits in crops for meeting the 4 pillars of sustainability (human, social, economic and
environmental). |
format |
Article |
author |
Kehinde, Okewu Emmanuel, Okewu Wong, Ling Shing Siti Sarah, Maidin |
author_facet |
Kehinde, Okewu Emmanuel, Okewu Wong, Ling Shing Siti Sarah, Maidin |
author_sort |
Kehinde, Okewu |
title |
Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development |
title_short |
Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development |
title_full |
Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development |
title_fullStr |
Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development |
title_full_unstemmed |
Genetic Algorithm for Forecasting Bioinformatic Outcomes of Mutation-induced Cowpeas for Sustainable Development |
title_sort |
genetic algorithm for forecasting bioinformatic outcomes of mutation-induced cowpeas for sustainable development |
publisher |
INTI International University |
publishDate |
2023 |
url |
http://eprints.intimal.edu.my/1062/1/jods2023_12.pdf http://eprints.intimal.edu.my/1062/ http://ipublishing.intimal.edu.my/jods.html |
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