  Machine Learning models are used in a wide variety of domains. However,
machine learning methods often require a large amount of data in order to be
successful. This is especially troublesome in domains where collecting
real-world data is difficult and/or expensive. Data simulators do exist for
many of these domains, but they do not sufficiently reflect the real world data
due to factors such as a lack of real-world noise. Recently generative
adversarial networks (GANs) have been modified to refine simulated image data
into data that better fits the real world distribution, using the SimGAN
method. While evolutionary computing has been used for GAN evolution, there are
currently no frameworks that can evolve a SimGAN. In this paper we (1) extend
the SimGAN method to refine one-dimensional data, (2) modify Easy Cartesian
Genetic Programming (ezCGP), an evolutionary computing framework, to create
SimGANs that more accurately refine simulated data, and (3) create new
feature-based quantitative metrics to evaluate refined data. We also use our
framework to augment an electrocardiogram (ECG) dataset, a domain that suffers
from the issues previously mentioned. In particular, while healthy ECGs can be
simulated there are no current simulators of abnormal ECGs. We show that by
using an evolved SimGAN to refine simulated healthy ECG data to mimic
real-world abnormal ECGs, we can improve the accuracy of abnormal ECG
classifiers.
