Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently then operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1,000X relative to the state-of-the-art sequential implementation.
@article{arxiv.2106.04034,
title = {GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming},
author = {Leonardo Trujillo and Jose Manuel Muñoz Contreras and Daniel E Hernandez and Mauro Castelli and Juan J Tapia},
journal= {arXiv preprint arXiv:2106.04034},
year = {2021}
}