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.

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arXiv:2406.01821, 2024.

@article{shahrzad:arxiv24, title={GPU-Accelerated Rule Evaluation and Evolution}, author={Hormoz Shahrzad and Risto Miikkulainen}, journal={arXiv:2406.01821}, month={ }, url="http://nn.cs.utexas.edu/?shahrzad:arxiv24", year={2024} }