This paper presents a new algorithm, Evolutionary eXploration of Augmenting
Memory Models (EXAMM), which is capable of evolving recurrent neural networks
(RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM,
MGU and UGRNN cells. EXAMM evolved RNNs to perform prediction of large-scale,
real world time series data from the aviation and power industries. These data
sets consist of very long time series (thousands of readings), each with a
large number of potentially correlated and dependent parameters. Four different
parameters were selected for prediction and EXAMM runs were performed using
each memory cell type alone, each cell type with feed forward nodes, and with
all possible memory cell types. Evolved RNN performance was measured using
repeated k-fold cross validation, resulting in 1210 EXAMM runs which evolved
2,420,000 RNNs in 12,100 CPU hours on a high performance computing cluster.
Generalization of the evolved RNNs was examined statistically, providing
interesting findings that can help refine the RNN memory cell design as well as
inform future neuro-evolution algorithms development.