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Genetic Algorithms in the Fields of Artificial Intelligence and Data Sciences

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  • Ayesha Sohail

    (Comsats University Islamabad, Lahore Campus)

Abstract

In the fields of engineering and data sciences, the optimization problems arise on regular basis. With the progress in the field of scientific computing and research, the optimization is not a problem for small data sets and lower dimensional problems. The problem arise, when the data is large, stochastic in nature, and/or multidimensional. The basic optimization tools fail for such problems due to the complexity. The genetic algorithms, based on the natural selection hypothesis, play an imperative role to deal with such complex problems. Genetic algorithms are used in the literature to optimize numerous problems. In the field of computational biology, these algorithms have provided cost effective solutions to find optimal values for large data sets. The genetic algorithms have been used for image reconstruction. These algorithms are based on sub-algorithms to improve the accuracy and precision. We will discuss the advanced genetic algorithms and their applications in detail. Genetic algorithm, in hybrid form have attracted interest of researchers from almost all fields, including computer science, applied mathematics, engineering and computational biology. These tools help to analyze the systems in a swift manner. This important feature is discussed with the aid of examples. The time series forecasting and the Bayesian inference, in combination with the genetic algorithms, can prove to be powerful artificial intelligence tools. We will discuss this important aspect in detail with the aid of some examples.

Suggested Citation

  • Ayesha Sohail, 2023. "Genetic Algorithms in the Fields of Artificial Intelligence and Data Sciences," Annals of Data Science, Springer, vol. 10(4), pages 1007-1018, August.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-021-00354-9
    DOI: 10.1007/s40745-021-00354-9
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    References listed on IDEAS

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    1. Zvi Drezner & Taly Dawn Drezner, 2020. "Biologically Inspired Parent Selection in Genetic Algorithms," Annals of Operations Research, Springer, vol. 287(1), pages 161-183, April.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    3. Yu, Zhenhua & Arif, Robia & Fahmy, Mohamed Abdelsabour & Sohail, Ayesha, 2021. "Self organizing maps for the parametric analysis of COVID-19 SEIRS delayed model," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
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    Cited by:

    1. Muhammad Tahir & Sufyan Ali & Ayesha Sohail & Ying Zhang & Xiaohua Jin, 2024. "Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects," Annals of Data Science, Springer, vol. 11(4), pages 1421-1434, August.

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