GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming
This work addresses performance bottlenecks for researchers and practitioners using GSGP in evolutionary computation, though it is incremental as it focuses on implementation optimization rather than new algorithmic insights.
The paper tackled the inefficiency in implementing Geometric Semantic Genetic Programming (GSGP) by developing GSGP-CUDA, a CUDA framework that exploits GPU parallelism, resulting in speedups over 1,000X compared to the state-of-the-art sequential implementation.
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.