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Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem

Author

Listed:
  • Nabeel Al-Milli

    (Department of Computer Science, King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
    These authors contributed equally to this work.)

  • Amjad Hudaib

    (Department of Computer Information Systems, King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
    These authors contributed equally to this work.)

  • Nadim Obeid

    (Department of Computer Information Systems, King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
    These authors contributed equally to this work.)

Abstract

Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration and exploitation while executing the search algorithm will enhance the overall performance of the searching algorithm. Exploration and exploitation are usually controlled offline by proper settings of parameters that affect the population-based algorithm performance. In this paper, we proposed a dynamic controller for one of the most well-known search algorithms, which is the Genetic Algorithm (GA). Population Diversity Controller-GA (PDC-GA) is proposed as a novel feature-selection algorithm to reduce the search space while building a machine-learning classifier. The PDC-GA is proposed by combining GA with k-mean clustering to control population diversity through the exploration process. An injection method is proposed to redistribute the population once 90% of the solutions are located in one cluster. A real case study of a bankruptcy problem obtained from UCI Machine Learning Repository is used in this paper as a binary classification problem. The obtained results show the ability of the proposed approach to enhance the performance of the machine learning classifiers in the range of 1 % to 4 % .

Suggested Citation

  • Nabeel Al-Milli & Amjad Hudaib & Nadim Obeid, 2021. "Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem," Mathematics, MDPI, vol. 9(8), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:823-:d:533476
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    References listed on IDEAS

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    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Luis M. Briceño-Arias & Giovanni Chierchia & Emilie Chouzenoux & Jean-Christophe Pesquet, 2019. "A random block-coordinate Douglas–Rachford splitting method with low computational complexity for binary logistic regression," Computational Optimization and Applications, Springer, vol. 72(3), pages 707-726, April.
    3. Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Natalie Weed & Trygve Bakken & Nile Graddis & Nathan Gouwens & Daniel Millman & Michael Hawrylycz & Jack Waters, 2019. "Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    6. Collins, Robert A. & Green, Richard D., 1982. "Statistical methods for bankruptcy forecasting," Journal of Economics and Business, Elsevier, vol. 34(4), pages 349-354.
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