Evolving SQL Queries from Examples with Developmental Genetic Programming

Large databases are becoming ever more ubiquitous, as are the opportunities for discovering useful knowledge within them. Evolutionary computation methods such as genetic programming have previously been applied to several aspects of the problem of discovering knowledge in databases. The more specific task of producing human-comprehensible SQL queries has several potential applications but has thus far been explored only to a limited extent. In this chapter we show howdevelopmental genetic programming can automatically generate SQL queries from sets of positive and negative examples. We show that a developmental genetic programming system can produce queries that are reasonably accurate while excelling in human comprehensibility relative to the well-known C5.0 decision tree generation system.