Genetic algorithm vs. gradient descent for training a neural network architecture dedicated to low data regimes in small medical datasets

ArXi:2605.27411v1 Announce Type: cross Aim/ Materials and Methods: We designed a spatial backpropagation scheme tailored to DEBI-NN and carried out a comparison between GD and GA for classification tasks, using a synthetic non-linear "two-moons" dataset, two clinical medical imaging radiomic datasets and a fetal cardiotocography dataset with a sample sizes ranging from n=85 to n=2126. Each optimizer was tuned through targeted hyperparameter searches adapted to each dataset.