This library originated from my need to handle 3D transformations, poses, trajectories, etc. from various software systems that often use different conventions. It has been developed for more than 7 years now and is in a fairly robust and well-tested state. It might be useful, for instance, in robotics, computer vision, motion capture, ...
An Amazon review is not comparable to a review in a scientific journal. In a scientific journal a review is done by experts of the field and they invest a couple of hours / days. Scientists do this because it is part of their job. It is a contribution to the scientific community.
This reminds me a lot of the work on compressed neural network from Jan Koutnik and his colleagues. They don't evolve topology of a NN, but they learn weights of a neural network in some compressed space. That seems to be very similar to weight sharing.
For example, in the case of the cart pole (without swing up) benchmark a simple linear controller with equal positive weights is required which can easily be encoded with this approach.
Overfitting is a well-known problem in the ML community. There are methods to avoid this: cross validation, train-test splits, etc. There are also models that give you an estimate of the standard deviation of a prediction. What is the point? We don't need new algorithms, we just have to apply existing methods properly.