  Evolutionary algorithms (EAs) provide unique advantages for optimizing neural
networks in complex search spaces. This paper introduces a new web platform,
NeuroEvo (neuroevo.io), that allows users to interactively design and train
neural network classifiers using evolutionary and particle swarm algorithms.
The classification problem and training data are provided by the user and, upon
completion of the training process, the best classifier is made available to
download and implement in Python, Java, and JavaScript. NeuroEvo is a
cloud-based application that leverages GPU parallelization to improve the speed
with which the independent evolutionary steps, such as mutation, crossover, and
fitness evaluation, are executed across the population. This paper outlines the
training algorithms and opportunities for users to specify design decisions and
hyperparameter settings. The algorithms described in this paper are also made
available as a Python package, neuroevo (PyPI:
https://pypi.org/project/neuroevo/).
