Evolutionary induction of global model trees with specialized operators and memetic extensions

Metaheuristics, such as evolutionary algorithms (EAEAs), have been successfully applied to the problem of decision tree induction. Recently, an EA was proposed to evolve model trees, which are a particular type of decision tree that is employed to solve regression problems. However, there is a need to specialize the EAEAs in order to exploit the full potential of evolutionary induction. The main contribution of this paper is a set of solutions and techniques that incorporates knowledge about the inducing problem for the global model tree into the evolutionary search. The objective of this paper is to demonstrate that specialized EAEA can find more accurate and less complex solutions to the traditional greedy-induced counterparts and the straightforward application of EAEA.This paper proposes a novel solution for each step of the evolutionary process and presents a new specialized EAEA for model tree induction called the Global Model Tree (GMTGMT). An empirical investigation shows that trees induced by the GMTGMT are one order of magnitude less complex than trees induced by popular greedy algorithms, and they are equivalent in terms of predictive accuracy with output models from straightforward implementations of evolutionary induction and state-of-the-art methods.