Soft Computing

, Volume 14, Issue 9, pp 953–971 | Cite as

CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems

  • María D. Perez-Godoy
  • Antonio J. Rivera
  • Francisco J. Berlanga
  • María José Del Jesus
Original Paper


This paper presents a new evolutionary cooperative–competitive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, CO2RBFN, promotes a cooperative–competitive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. The proposal considers, in order to measure the credit assignment of an individual, three factors: contribution to the output of the complete RBFN, local error and overlapping. In addition, to decide the operators’ application probability over an RBF, the algorithm uses a Fuzzy Rule Based System. It must be highlighted that the evolutionary algorithm considers a distance measure which deals, without loss of information, with differences between nominal features which are very usual in classification problems. The precision and complexity of the network obtained by the algorithm are compared with those obtained by different soft computing methods through statistical tests. This study shows that CO2RBFN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other methods considered.


Radial Basis Function Networks Evolutionary Algorithms Cooperative–Competitive Evolutionary Design Classification Fuzzy Rule Base Systems 



This work has been partially supported by the CICYT Spanish Projects TIN2005-04386-C05-03, TIN2007-60587 and the Andalusian Research Plan TIC-3928.


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • María D. Perez-Godoy
    • 1
  • Antonio J. Rivera
    • 1
  • Francisco J. Berlanga
    • 1
  • María José Del Jesus
    • 1
  1. 1.Departamento de InformáticaUniversidad de JaénJaénSpain

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