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A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

Daniela Sánchez1, Patricia Melin1, Oscar Castillo1

  • 1Tijuana Institute of Technology, Tijuana, BC, Mexico.

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A novel grey wolf optimizer enhances modular neural network (MNN) design for human recognition. This granular approach optimizes data and network architecture, outperforming other evolutionary algorithms in biometric tests.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Evolutionary computing offers bio-inspired algorithms for complex optimization problems.
  • Modular Neural Networks (MNNs) provide a flexible architecture for various tasks.
  • Human recognition relies on accurate and robust biometric identification systems.

Purpose of the Study:

  • To propose a grey wolf optimizer for designing modular granular neural networks (MGNNs).
  • To optimize MGNN architecture parameters for effective human recognition.
  • To compare the proposed method against genetic and firefly algorithms.

Main Methods:

  • Optimal granulation of data for MGNN training.
  • Grey Wolf Optimizer (GWO) for automated MNN architecture design.
  • Benchmarking on ear, iris, and face biometric datasets.

Main Results:

  • The proposed grey wolf optimizer effectively designs modular granular neural networks.
  • The MGNN approach demonstrated strong performance in human recognition tasks.
  • Comparative analysis showed advantages over genetic and firefly algorithms.

Conclusions:

  • The grey wolf optimizer is a viable and effective technique for designing MGNNs for human recognition.
  • Granular computing combined with GWO offers a powerful approach for biometric systems.
  • Further research can explore GWO's application in other complex AI domains.