Genetic Mutation of Cowpea as a Constrained Stochastic Optimization Problem in Sustainability
The search for desirable qualities in crop using non-natural breeding techniques like genetic mutation has to ensure a balance between the pillars of sustainability (human, social, economic and environmental)- Candidate optimization crop breeds target food security and sufficiency for humans,...
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| Format: | Article |
| Language: | English |
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INTI International University
2023
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| Online Access: | http://eprints.intimal.edu.my/1816/ http://eprints.intimal.edu.my/1816/1/jods2023_13.pdf |
| _version_ | 1848766843009892352 |
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| author | Emmanuel, Okewu Kehinde, Okewu Siti Sarah, Maidin Wong, Ling Shing |
| author_facet | Emmanuel, Okewu Kehinde, Okewu Siti Sarah, Maidin Wong, Ling Shing |
| author_sort | Emmanuel, Okewu |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | The search for desirable qualities in crop using non-natural breeding techniques like genetic
mutation has to ensure a balance between the pillars of sustainability (human, social, economic
and environmental)- Candidate optimization crop breeds target food security and sufficiency for
humans, improved income-earning capacity of farmers, better social (societal) interactions, and
environmental protection. This is to ensure we meet the needs of the present generation while not
compromising on the needs of future generations. However, uncertainties surround the genetic
engineering process, potentially making genetic mutation for sustainability a constrained
stochastic optimization (CSO) problem. Using series of experiments in Python programming, we
applied genetic algorithm to the genetic mutation of cowpea, a tropical leguminous plant and
protein-rich crop. Our experiments with genetic algorithm as a stochastic optimizer, confirmed
that the evolution from the initial random string (initial cowpea species) to the target string
(optimal cowpea solution) was smeared by uncertainties in the optimization-for-sustainability
effort. In any case, cowpeas with the desired qualities of drought tolerance and high yield gradually
emerged as we progressed from the first generation (M1) to subsequent generations with the aim
of meeting the sustainability targets. |
| first_indexed | 2025-11-14T11:57:35Z |
| format | Article |
| id | intimal-1816 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:57:35Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-18162023-11-15T05:43:36Z http://eprints.intimal.edu.my/1816/ Genetic Mutation of Cowpea as a Constrained Stochastic Optimization Problem in Sustainability Emmanuel, Okewu Kehinde, Okewu Siti Sarah, Maidin Wong, Ling Shing Q Science (General) QA76 Computer software QH426 Genetics The search for desirable qualities in crop using non-natural breeding techniques like genetic mutation has to ensure a balance between the pillars of sustainability (human, social, economic and environmental)- Candidate optimization crop breeds target food security and sufficiency for humans, improved income-earning capacity of farmers, better social (societal) interactions, and environmental protection. This is to ensure we meet the needs of the present generation while not compromising on the needs of future generations. However, uncertainties surround the genetic engineering process, potentially making genetic mutation for sustainability a constrained stochastic optimization (CSO) problem. Using series of experiments in Python programming, we applied genetic algorithm to the genetic mutation of cowpea, a tropical leguminous plant and protein-rich crop. Our experiments with genetic algorithm as a stochastic optimizer, confirmed that the evolution from the initial random string (initial cowpea species) to the target string (optimal cowpea solution) was smeared by uncertainties in the optimization-for-sustainability effort. In any case, cowpeas with the desired qualities of drought tolerance and high yield gradually emerged as we progressed from the first generation (M1) to subsequent generations with the aim of meeting the sustainability targets. INTI International University 2023-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1816/1/jods2023_13.pdf Emmanuel, Okewu and Kehinde, Okewu and Siti Sarah, Maidin and Wong, Ling Shing (2023) Genetic Mutation of Cowpea as a Constrained Stochastic Optimization Problem in Sustainability. Journal of Data Science, 2023 (13). pp. 1-16. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | Q Science (General) QA76 Computer software QH426 Genetics Emmanuel, Okewu Kehinde, Okewu Siti Sarah, Maidin Wong, Ling Shing Genetic Mutation of Cowpea as a Constrained Stochastic Optimization Problem in Sustainability |
| title | Genetic Mutation of Cowpea as a Constrained Stochastic Optimization
Problem in Sustainability |
| title_full | Genetic Mutation of Cowpea as a Constrained Stochastic Optimization
Problem in Sustainability |
| title_fullStr | Genetic Mutation of Cowpea as a Constrained Stochastic Optimization
Problem in Sustainability |
| title_full_unstemmed | Genetic Mutation of Cowpea as a Constrained Stochastic Optimization
Problem in Sustainability |
| title_short | Genetic Mutation of Cowpea as a Constrained Stochastic Optimization
Problem in Sustainability |
| title_sort | genetic mutation of cowpea as a constrained stochastic optimization
problem in sustainability |
| topic | Q Science (General) QA76 Computer software QH426 Genetics |
| url | http://eprints.intimal.edu.my/1816/ http://eprints.intimal.edu.my/1816/ http://eprints.intimal.edu.my/1816/1/jods2023_13.pdf |