Resumen
Knowledge Discovery in Databases (KDD) process consists of many stages among which the main one is Data Mining (DM). There are many DM tasks but the discovery of classification rules is the most known. The classification task can be addressed by conventional algorithms (e.g., statistics) or by artificial intelligence techniques (e.g., neural networks, evolutionary algorithms, etc.). In this research we are interested in investigating the applicability of a hybrid combination of genetic algorithms and fuzzy sets to find rules in large and complex spaces. This paper reviews some hybrid Genetic-Fuzzy approaches for the extraction of classification rules found in the literature and discusses the possibility of adapting them to knowledge discovery in Science and Technology (S&T) databases