  In this paper, we resort to the TensorFlow framework to investigate the
benefits of applying data vectorization and fitness caching methods to domain
evaluation in Genetic Programming. For this purpose, an independent engine was
developed, TensorGP, along with a testing suite to extract comparative timing
results across different architectures and amongst both iterative and
vectorized approaches. Our performance benchmarks demonstrate that by
exploiting the TensorFlow eager execution model, performance gains of up to two
orders of magnitude can be achieved on a parallel approach running on dedicated
hardware when compared to a standard iterative approach.
