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Novel elegant fuzzy genetic algorithms in classification problems

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Abstract

In this paper, we propose three novel algorithms such as Novel genetic algorithm complex-valued backpropagation neural network (GA-CVBNN), Novel elegant fuzzy genetic algorithm (EFGA) and elegant fuzzy genetic algorithm-based complex-valued backpropagation neural network (EFGA-CVBNN) for classification of accuracy in datasets. In GA-CVBNN, classical Genetic Algorithm has been used for selecting appropriate initial weights for CVBNN. The EFGA is developed to resolve the drawback of classical GA by employing fuzzy logic to control parameters and selective pressure of GA. The EFGA uses a Min-Heap data structure and Pareto principle to improve the classical genetic algorithm. The EFGA-CVBNN resolves the drawbacks of classical CVBNN by employing EFGA at the time of initial weight selection. From the simulation result, the GA-CVBNN performs better than existing CVBNN and it is not efficient. To enhance the performance of GA-CVBNN, we have developed EFGA-CVBNN. Experimental results on various synthetic datasets and benchmark datasets taken from UCI machine learning repository shows that EFGA-CVBNN outperforms PSO-CVBNN in terms of classification accuracy and time. Statistical t test has been used to validate the obtained results.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Farmagudi, Goa, India

    K. Venkatanareshbabu & S. Nisheel

  2. Department of Mathematics, Sungkyunkwan University, Suwon, 440-746, South Korea

    R. Sakthivel

  3. Department of Mathematics, Anna University Regional Campus, Coimbatore, Tamil Nadu, 641046, India

    K. Muralitharan

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  1. K. Venkatanareshbabu
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  2. S. Nisheel
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  3. R. Sakthivel
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  4. K. Muralitharan
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Correspondence to R. Sakthivel.

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Communicated by V. Loia.

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Venkatanareshbabu, K., Nisheel, S., Sakthivel, R. et al. Novel elegant fuzzy genetic algorithms in classification problems. Soft Comput 23, 5583–5603 (2019). https://doi.org/10.1007/s00500-018-3216-8

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  • DOI: https://doi.org/10.1007/s00500-018-3216-8

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