Introduction - If you have any usage issues, please Google them yourself
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