Machine Learning algorithms have been extensively researched throughout the
last decade, leading to unprecedented advances in a broad range of
applications, such as image classification and reconstruction, object
recognition, and text categorization. Nonetheless, most Machine Learning
algorithms are trained via derivative-based optimizers, such as the Stochastic
Gradient Descent, leading to possible local optimum entrapments and inhibiting
them from achieving proper performances. A bio-inspired alternative to
traditional optimization techniques, denoted as meta-heuristic, has received
significant attention due to its simplicity and ability to avoid local optimums
imprisonment. In this work, we propose to use meta-heuristic techniques to
fine-tune pre-trained weights, exploring additional regions of the search
space, and improving their effectiveness. The experimental evaluation comprises
two classification tasks (image and text) and is assessed under four literature
datasets. Experimental results show nature-inspired algorithms' capacity in
exploring the neighborhood of pre-trained weights, achieving superior results
than their counterpart pre-trained architectures. Additionally, a thorough
analysis of distinct architectures, such as Multi-Layer Perceptron and
Recurrent Neural Networks, attempts to visualize and provide more precise
insights into the most critical weights to be fine-tuned in the learning
process.

使用启发式元算法优化预训练权重可改善机器学习分类任务的结果。