  Genetic Programming (GP) is known to suffer from the burden of being
computationally expensive by design. While, over the years, many techniques
have been developed to mitigate this issue, data vectorization, in particular,
is arguably still the most attractive strategy due to the parallel nature of
GP. In this work, we employ a series of benchmarks meant to compare both the
performance and evolution capabilities of different vectorized and iterative
implementation approaches across several existing frameworks. Namely, TensorGP,
a novel open-source engine written in Python, is shown to greatly benefit from
the TensorFlow library to accelerate the domain evaluation phase in GP. The
presented performance benchmarks demonstrate that the TensorGP engine manages
to pull ahead, with relative speedups above two orders of magnitude for
problems with a higher number of fitness cases. Additionally, as a consequence
of being able to compute larger domains, we argue that TensorGP performance
gains aid the discovery of more accurate candidate solutions.

Neural and Evolutionary Computing;Machine Learning