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Evolutionary optimization of RBF networks.

E Lacerda1, A de Carvalho, T Ludermir

  • 1Informatics Center, UFPE, Brazil. egml@cin.ufpe.br

International Journal of Neural Systems
|September 29, 2001
PubMed
Summary
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Optimizing artificial neural networks (ANNs) is challenging. This study proposes a new genetic algorithm strategy to effectively define parameters for Radial Basis Function (RBF) networks, improving their performance.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Science

Background:

  • Defining free parameters for artificial neural networks (ANNs) hinders widespread adoption.
  • Radial Basis Function (RBF) networks offer a potential solution but require effective parameter optimization.

Purpose of the Study:

  • To explore the use of genetic algorithms (GAs) for optimizing RBF network parameters.
  • To introduce a novel GA strategy for RBF network optimization.

Main Methods:

  • Genetic algorithms (GAs) were employed to optimize RBF network parameters.
  • A new representation, crossover operator, and multiobjective optimization criterion were developed for the GA.
  • Experiments were conducted on a benchmark problem.

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Main Results:

  • The proposed GA strategy demonstrated effectiveness in optimizing RBF network parameters.
  • Performance of the new model was compared against existing approaches on a benchmark task.

Conclusions:

  • Genetic algorithms provide a viable method for defining RBF network parameters.
  • The novel GA strategy offers an improved approach to RBF network optimization.