Abstract
Cartesian Genetic Programming of Artificial Neural Networks is a NeuroEvolutionary method based on Cartesian Genetic Programming. Cartesian Genetic Programming has recently been extended to allow recurrent connections. This work investigates applying the same recurrent extension to Cartesian Genetic Programming of Artificial Neural Networks in order to allow the evolution of recurrent neural networks. The new Recurrent Cartesian Genetic Programming of Artificial Neural Networks method is applied to the domain of series forecasting where it is shown to significantly outperform all standard forecasting techniques used for comparison including autoregressive integrated moving average and multilayer perceptrons. An ablation study is also performed isolating which specific aspects of Recurrent Cartesian Genetic Programming of Artificial Neural Networks contribute to it’s effectiveness for series forecasting.
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