An initialization method for grammatical evolution assisted by decision trees
Igor L.S. Russo, Heder S. Bernardino, Carlos Cristiano Hasenclever Borges, Hélio J. C. Barbosa · 2016
Grammatical Evolution (GE) is a genetic programming technique in which the candidate solutions are represented using a binary genotype and the programs can be generated through production rules of a formal grammar. Similarly to other evolutionary computation methods, the GE's performance can be improved when an adequate initial population seeding is adopted. Decision trees are widely used to model classifiers in machine learning and their symbolic form can be mapped back to the GE's binary representation of the candidate individuals. Thus, the use of machine learning techniques to generate decision trees to compose the initial population of GE is investigated here. Computational experiments with a real world data set are carried out and the results show an increase of performance when compared to the traditional seeding approach.