Provides algorithms for learning various types of classification trees using evolutionary search. Includes decision trees with single and multi-attribute tests, cost-sensitive learning, and provides an extensive framework for running and comparing classification algorithms. Runs cross-validation automatically and outputs gnuplot files when several algorithms are evaluated. Written in portable C++, compiles in Linux and Windows in Visual Studio, reads data in the C4.5 input format.