A method for handling numerical attributes in GA-based inductive concept learners

Abstract. This paper proposes a method for dealing with numerical attributes in inductive concept learning systems based on genetic algo-rithms. The method uses constraints for restricting the range of values of the attributes and novel stochastic operators for modifying the con-straints. These operators exploit information on the distribution of the values of an attribute. The method is embedded into a GA based system for inductive logic programming. Results of experiments on various data sets indicate that the method provides an effective local discretization tool for GA based inductive concept learners.