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Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training

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Abstract

Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection.

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Acknowledgements

The paper is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, Belgrade, 11000, Serbia

    Nebojsa Bacanin & Timea Bezdan

  2. Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey

    Fadi Al-Turjman

  3. Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, KRG, Iraq

    Tarik A. Rashid

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  1. Nebojsa Bacanin
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  2. Timea Bezdan
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  4. Tarik A. Rashid
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Correspondence to Nebojsa Bacanin.

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Bacanin, N., Bezdan, T., Al-Turjman, F. et al. Artificial Flora Optimization Algorithm with Genetically Guided Operators for Feature Selection and Neural Network Training. Int. J. Fuzzy Syst. 24, 2538–2559 (2022). https://doi.org/10.1007/s40815-021-01191-x

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  • DOI: https://doi.org/10.1007/s40815-021-01191-x

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