Evolutionary induction of global model trees with specialized operators and memetic extensions
Metaheuristics, such as evolutionary algorithms (EA EA s), 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 EA EA s 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 EA EA can find more accurate and less complex solutions to the traditional greedy-induced counterparts and the straightforward application of EA EA . This paper proposes a novel solution for each step of the evolutionary process and presents a new specialized EA EA for model tree induction called the Global Model Tree (GMT GMT ). An empirical investigation shows that trees induced by the GMT GMT 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.
model tree evolutionary algorithm multiple linear regression machine learning