Ullah, S and Masood, M orcid.org/0000-0003-4388-569X (2023) Genetic Drift and its Effects on the Performance of Genetic Algorithm(GA). In: 2023 International Conference on Robotics and Automation in Industry (ICRAI). 2023 International Conference on Robotics and Automation in Industry (ICRAI), 03-05 Mar 2023, Peshawar, Pakistan. IEEE ISBN 978-1-6654-6472-7
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Evolutionary Algorithm, Genetic Algorithm, Max One, Genetic Drift |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 May 2023 08:13 |
Last Modified: | 26 May 2023 08:13 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icrai57502.2023.10089573 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199244 |