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A new and fast rival genetic algorithm for feature selection

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Abstract

Feature selection is one of the significant steps in classification tasks. It is a pre-processing step to select a small subset of significant features that can contribute the most to the classification process. Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. The genetic algorithm (GA) as a fundamental optimization tool has been widely used in feature selection tasks. However, GA suffers from the hyperparameter setting, high computational complexity, and the randomness of selection operation. Therefore, we propose a new rival genetic algorithm, as well as a fast version of rival genetic algorithm, to enhance the performance of GA in feature selection. The proposed approaches utilize the competition strategy that combines the new selection and crossover schemes, which aim to improve the global search capability. Moreover, a dynamic mutation rate is proposed to enhance the search behaviour of the algorithm in the mutation process. The proposed approaches are validated on 23 benchmark datasets collected from the UCI machine learning repository and Arizona State University. In comparison with other competitors, proposed approach can provide highly competing results and overtake other algorithms in feature selection.

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Acknowledgements

The authors would like to thank the Skim Zamalah UTeM for supporting this research.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100, Melaka, Malaysia

    Jingwei Too & Abdul Rahim Abdullah

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  1. Jingwei Too
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  2. Abdul Rahim Abdullah
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JT took part in conceptualization, methodology, formal analysis and investigation, writing the original draft, and resource allocation; JT and ARA were involved in writing, reviewing, and editing; ARA conducted supervision.

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Correspondence to Jingwei Too.

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Too, J., Abdullah, A.R. A new and fast rival genetic algorithm for feature selection. J Supercomput 77, 2844–2874 (2021). https://doi.org/10.1007/s11227-020-03378-9

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