Genetic Mutation of Cowpea as a Constrained Stochastic Optimization Problem in Sustainability

Abstract

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.