Work

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

Year: 2025

Type: article

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.

Source: Biomimetics

DOI: 10.3390/biomimetics10110762

Authors: Fabián Pizarro, Emanuel Vega, Ricardo Soto, Broderick Crawford, José Villamayor

Institution: Pontificia Universidad Católica de Valparaíso

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,654

Funder: Pontificia Universidad Católica de Valparaíso