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Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic

Alan J Barton1, Julio J Valdés, Robert Orchard

  • 1Knowledge Discovery Group, Institute for Information Technology, National Research Council Canada, Ottawa, Ontario, Canada. alan.barton@nrc-cnrc.gc.ca

Neural Networks : the Official Journal of the International Neural Network Society
|July 17, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel neural network (NN-GP) where neuron functions are learned via Genetic Programming, enabling flexible network structures without traditional weights or biases. This approach shows promise for classification tasks by creating simplified representations of complex data.

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Classical neural networks rely on pre-specified neuron models and fixed architectures.
  • Existing models often involve complex weight and bias tuning, limiting adaptability.
  • The need for more flexible and automatically evolving neural network architectures is evident.

Purpose of the Study:

  • To present a new type of neural network, termed NN-GP, that autonomously learns neuron functions.
  • To demonstrate a flexible network architecture where any interconnection is possible.
  • To explore a model that eschews traditional weights and biases, simplifying the learning process.

Main Methods:

  • Utilized Genetic Programming (GP) to evolve general functions for individual neurons.

Related Experiment Videos

  • Implemented an Evolutionary Computation process to search the function space for optimal neuron models.
  • Evaluated network performance using fitness measures based on classification or regression errors.

    • Main Results:

    • Developed NN-GP networks capable of learning unique neuron models.
    • Achieved flexible network layouts without reliance on connection weights or biases.
    • Demonstrated promising performance on real-world classification problems through dimensionality reduction.

    Conclusions:

    • NN-GP offers a novel approach to neural network design by evolving neuron functions.
    • The method facilitates the creation of low-dimensional representations from high-dimensional data.
    • This evolutionary approach holds potential for advancing classification and regression tasks.