The use of cultural algorithms with evolutionary programming to control the data mining of large-scale spatio-temporal databases

We use an evolutionary computational approach based upon cultural algorithms to guide the incremental learning decision trees by ITI. The results are compared to those produced by ITI itself for a complex real-world database. The results suggest that ITI can indeed produce optimal trees in some cases, and can produce optimal trees using an evolutionary approach in others.