Elsevier

SoftwareX

Volume 18, June 2022, 101085
SoftwareX

Original software publication
GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming

https://doi.org/10.1016/j.softx.2022.101085Get rights and content
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open access

Abstract

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 than 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 1,000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems.

Keywords

Genetic Programming
Geometric Semantic Genetic Programming
CUDA
GPU

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