Data mining has become an important research topic. The increasing use of computers results in an explosion of information. These data can be best used if the knowledge hidden can be uncovered. Thus there is a need for a way to automatically discover knowledge from data. New approaches for knowledge discovery from two medical databases are investigated. Two different kinds of knowledge, namely rules and causal structures, are learned. Rules capture interesting patterns and regularities in the databases. Causal structures represented by Bayesian networks capture the causality relationships among the attributes. We employ advanced evolutionary algorithms for these discovery tasks. In particular, generic genetic programming is employed as a rule learning algorithm. Our approach for discovering causality relationships is based on evolutionary programming which learns Bayesian network structures.
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