Evolving SQL queries for data mining

This paper presents a methodology for applying the principles of evolutionary computation to knowledge discovery in databases by evolving SQL queries that describe datasets. In our system, the fittest queries are rewarded by having their attributes being given a higher probability of surviving in subsequent queries. The advantages of using SQL queries include their readability for non-experts and ease of integration with existing databases. The evolutionary algorithm (EA) used in our system is very different from existing EAs, but seems to be effective and efficient according to the experiments to date with three different testing data sets.