GPU-Accelerated Rule Evaluation and Evolution

TL;DR

This paper presents AERL, a GPU-accelerated approach to improve the efficiency and scalability of evolutionary rule-based machine learning by leveraging tensorized rule representations and back-propagation for rule optimization.

Contribution

AERL introduces GPU-optimized rule evaluation and coefficient tuning, significantly enhancing the speed and effectiveness of ERL systems for explainable AI.

Findings

AERL accelerates fitness evaluation using GPU tensorization.

AERL improves rule search effectiveness through back-propagation.

Experimental results show faster and more effective rule learning.

Abstract

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.