  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.
