2021/06/08 by Leonardo Trujillo, Trujillo, Leonardo, José Manuel Muñoz Contreras +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #I.2.2 #I.5.5 #Machine Learning (cs.LG) #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Performance (cs.PF) #Protein Degradation and Inhibitors
paper · pdf · doi:10.48550/arxiv.2106.04034
openalex publication_date 2021/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.