Recurrent Cartesian Genetic Programming Applied to Series Forecasting
Andrew James Turner, Julian Francis Miller · 2015
Recurrent Cartesian Genetic Programming is a recently proposed extension to Cartesian Genetic Programming which allows cyclic program structures to be evolved. We apply both standard and Recurrent Cartesian Genetic Programming to the domain of series forecasting. Their performance is then compared to a number of well-known classical forecasting approaches. Our results show that not only does Recurrent Cartesian Genetic Programming outperform standard Cartesian Genetic Programming, but it also outperforms many standard forecasting techniques.