GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming
Tecnológico Nacional de México/IT de Tijuana（墨西哥国家技术学院/蒂华纳技术学院）；NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa（里斯本新大学NOVA信息管理学院）；Instituto Politécnico Nacional - CITEDI（国家理工学院-CITEDI）
英文摘要：
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.
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