hallenge to the use of supervised neural networks in data mining applications

is to get explicit knowledge from these models. For this purpose, a clustering genetic algorithm

for rule extraction from artiÞ cial neural networks is developed. The methodology is based on the

clustering of the hidden unit activation values. A simple encoding scheme that yields to constant-

length chromosomes is used, thus allowing the application of the standard genetic operators. Besides,

a consistent algorithm to avoid some of the drawbacks of this kind of representation is also developed.

The individual Þ tness is determined b