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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Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
INTI International University
2023
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Subjects: | |
Online Access: | 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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Summary: | 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). |
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