Novel Performance-Based Hyperparameter Optimization with the Use of Bounding Box Tuner (BBT)

Hyperparameter search was found not making good use of compute resources as surrogate-based optimizers consume extensive memory and demand long set-up time.Meanwhile, projects running with fixed budgets require lean tuning tools.The current study presents Bounding Box Tuner (BBT) and conducts tests of its capability to attain maximum validation accuracy while reducing tuning time and memory use.The project team compared BBT with Random Search, Gaussian Processes for Bayesian Optimization, Tree-Structured Parzen Estimator (TPE), Evolutionary Search and Local Search to decide on the optimum option.Modified National Institute of Standards and Technology (MNIST) classification with a multilayer perceptron (0.11 M weights) and Tiny Vision Transformer (TinyViT) (9.5 M weights) were adopted.Each optimizer was assigned to run 50 trials.During the trial, early pruning stopped a run if validation loss rose for four epochs.All tests applied one NVIDIA GTX 1650 Ti GPU; the key metrics for measurement included best validation accuracy, total search time, and time per trial.As regards the perceptron task, BBT reached 97.88% validation accuracy in 1994 s whereas TPE obtained 97.98% in 2976 s.Concerning TinyViT, BBT achieved 94.92% in 2364 s, and GP-Bayesian gained 94.66% in 2191 s.It was discovered that BBT kept accuracy within 0.1 percentage points of the best competitor and reduced tuning time by one-third.The algorithm renders the surrogate model unnecessary, enforces constraints by design and exposes solely three user parameters.Supported by the evidence of these benefits, BBT was considered to be a practical option for rapid and resource-aware hyperparameter optimization in deep-learning pipelines.