Abstract
This paper presents an implementation of Grammatical Evolution on a GPU architecture. Our proposal, Embedded Grammars, implements the grammar directly in the code. Although more rigid, it allows to compute the decodification in parallel with the evaluation of the individuals. We tested three different grammars with a set of eight symbolic regression problems. The symbolic regression problems consists on obtaining a mathematical expression in the form y=f(x) , in our case, from a set of 288 pairs x, y. The analysis of the results shows that Embedded Grammars are better not only in terms of execution time, but also in quality when compared with an implementation on a CPU. Speed-up results are also better than those presented in the literature.
J. Manuel Colmenar
Associate Professor
My research interests are focused on metaheuristics applied to optimization problems. I have worked on different combinatorial optimization problems applying trajectorial algorithms such us GRASP or VNS. Besides, I am very interested in applications of Grammatical Evolution, specifically in model and prediction domain, as alternative to machine learning approaches.