A genetic algorithm (GA) is a meta-heuristic computation method that is inspired by Darwin's theory of evolution. GA has a promising future in optimization and search problems. It has caught the interest of researchers in the fields of data science, artificial intelligence, and mathematics among many others. GA depends on various operators which include parent selection, crossover, and mutation. The crossover and mutation operators incorporate diversity in the population. GA has a dependency on genetic diversity just like thriving species of any habitat. In the natural world, isolated species and small populations amplify genetic drift, increasing their chances of loss of alleles including beneficial ones. Existing research in GA has an emphasis on natural selection, however, another mechanism of evolution i.e., Genetic drift is not studied in GA. Genetic drift, like in nature, also affects genetic algorithms as it mimics natural processes. Genetic drift causes fixation of alleles and loss of diversity, making GA provide a sub-optimal solution. This research establishes the negative effects of demographic restrictions on the population as observed in the natural world. Subsequently establishes a link between research in biodiversity and evolution in the natural world to enhance the performance of GA in the digital world.