Heuristic Crossover Operator for Evolutionary Induced Decision Trees
Sašo Karakatič, Vili Podgorelec · 2014
In this paper we propose an innovative and improved variation of genetic operator crossover for the classification decision tree models. Our improved crossover operator uses heuristic to choose the tree node that is exchanged to construct the children solutions. The algorithm selects a single node based on the classification accuracy and the usage of that particular node. We evaluate this method by comparing it with the results of the standard crossover method where nodes for exchange are chosen at random.