A data scientist is optimizing hyperparameters using an iterative optimization algorithm, with each unique set of hyperparameters trained on a separate compute node. Despite conducting eight evaluations across eight nodes, there's no consistent improvement in model accuracy. What single change could most effectively enhance the model's accuracy during the tuning process?