GPU-Accelerated Rule Evaluation and Evolution
<|reference_start|>GPU-Accelerated Rule Evaluation and Evolution: This paper introduces an innovative approach to boost the efficiency and scalability of Evolutionary Rule-based machine Learning (ERL), a key technique in explainable AI. While traditional ERL systems can distribute processes across multiple CPUs, fitness evaluation of candidate rules is a bottleneck, especially with large datasets. The method proposed in this paper, AERL (Accelerated ERL) solves this problem in two ways. First, by adopting GPU-optimized rule sets through a tensorized representation within the PyTorch framework, AERL mitigates the bottleneck and accelerates fitness evaluation significantly. Second, AERL takes further advantage of the GPUs by fine-tuning the rule coefficients via back-propagation, thereby improving search space exploration. Experimental evidence confirms that AERL search is faster and more effective, thus empowering explainable artificial intelligence.<|reference_end|>
@article{shahrzad2024gpu-accelerated,
    title={GPU-Accelerated Rule Evaluation and Evolution},
    author={Hormoz Shahrzad and Risto Miikkulainen},
    journal={arXiv preprint arXiv:2406.01821},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01821},
    primaryClass={cs.NE}
}