Soft Computing

, Volume 23, Issue 14, pp 5583–5603 | Cite as

Novel elegant fuzzy genetic algorithms in classification problems

  • K. Venkatanareshbabu
  • S. Nisheel
  • R. SakthivelEmail author
  • K. Muralitharan
Methodologies and Application


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.


Classification Complex number Fuzzy logic Genetic algorithm Neural network Optimization 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • K. Venkatanareshbabu
    • 1
  • S. Nisheel
    • 1
  • R. Sakthivel
    • 2
    Email author
  • K. Muralitharan
    • 3
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyFarmagudiIndia
  2. 2.Department of MathematicsSungkyunkwan UniversitySuwonSouth Korea
  3. 3.Department of MathematicsAnna University Regional CampusCoimbatoreIndia

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