GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming

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. Effic...

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Bibliographic Details
Published inarXiv.org
Main Authors Trujillo, Leonardo, Muñoz Contreras, Jose Manuel, Hernandez, Daniel E, Castelli, Mauro, Tapia, Juan J
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.06.2021
Subjects
Computer Science - Learning
Computer Science - Neural and Evolutionary Computing
Computer Science - Performance
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Machine learning
Semantics
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2106.04034