  Genetic Programming (GP) is an evolutionary algorithm commonly used for
machine learning tasks. In this paper we present a method that allows GP to
transform the representation of a large-scale machine learning dataset into a
more compact representation, by means of processing features from the original
representation at individual level. We develop as a proof of concept of this
method an autoencoder. We tested a preliminary version of our approach in a
variety of well-known machine learning image datasets. We speculate that this
method, used in an iterative manner, can produce results competitive with
state-of-art deep neural networks.
