<ul><li>Foxtsage is a hybrid optimization approach that integrates Hybrid FOX-TSA with Stochastic Gradient Descent for training Multi-Layer Perceptron models.</li><li>Foxtsage achieves a 42.03% reduction in loss mean and a 42.19% improvement in loss standard deviation compared to the widely adopted Adam optimizer.</li><li>There are modest improvements in accuracy, precision, recall, and F1-score with Foxtsage.</li><li>However, Foxtsage has a higher computational cost with a 330.87% increase in time mean compared to Adam.</li></ul>

Foxtsage vs. Adam: Revolution or Evolution in Optimization?

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