Abstract
This paper presents a new cooperative-coevolutive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm promotes a coevolutive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. As credit assignment three quality factors are considered which measure the role of the RBFs in the whole RBFN. In order to calculate the application probability of the coevolutive operators a Fuzzy Rule Base System has been used. The algorithm evaluation with different datasets has shown promising results.
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Pérez-Godoy, M.D., Rivera, A.J., del Jesus, M.J., Rojas, I. (2007). CoEvRBFN: An Approach to Solving the Classification Problem with a Hybrid Cooperative-Coevolutive Algorithm. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_40
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DOI: https://doi.org/10.1007/978-3-540-73007-1_40
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