Low-Overhead Learning: Quantized Shallow Neural Networks at the Service of Genetic Algorithm Optimization

TL;DR

This paper introduces a low-cost, efficient neural network to improve genetic algorithm performance by dynamically adjusting mutation and crossover rates.

Contribution

A quantized shallow neural network is proposed to reduce computational overhead in genetic algorithm optimization.

Findings

Quantized SNNs significantly reduce execution time while maintaining high-quality solutions.

The approach outperforms alternative shallow learning methods on 15 benchmark functions.

Abstract

Online parameter tuning significantly enhances the performance of optimization algorithms by dynamically adjusting mutation and crossover rates. However, current approaches often suffer from high computational costs and limited adaptability to complex and dynamic fitness landscapes, particularly when machine learning methods are employed. This work proposes a quantized shallow neural network (SNN) as an efficient learning-based component for dynamically adjusting the mutation and crossover rates of a genetic algorithm (GA). By leveraging runtime-generated data and applying quantization techniques like Quantization-aware Training (QaT) and Post-training Quantization (PtQ), the proposed approach reduces computational overhead while maintaining competitive performance. Experimental evaluation on 15 continuous benchmark functions demonstrates that the quantized SNN achieves high-quality solutions while significantly reducing execution time compared to alternative shallow learning methods. This study highlights the potential of quantized SNNs to balance efficiency and performance, broadening the applicability of shallow learning in optimization.

Figures