Multi-Objective Optimization of Hyperparameter Tuning
Advisor(s)
Dr. Ian Kropp
Confirmation
1
Document Type
Poster
Location
ONU McIntosh Center; Activities Room
Start Date
11-4-2025 12:00 PM
End Date
11-4-2025 12:50 PM
Abstract
Hyperparameter tuning is crucial in optimizing deep learning models, often requiring a balance between computational efficiency and model performance. This research explores multi-objective optimization performance for hyperparameter tuning, focusing on the trade-off between compiling time and the resulting model accuracy. To achieve results in this research, we used the Pymoo library, a Python library used for multi-objective optimization, and its documentation in order to amend previously worked-on problems to fit our needs. A custom-made dataset was used where a default configuration of a set image classification model using simple CNNs where hyperparameters are systematically altered upon running the model, recording of the training time and resulting accuracy of the model was done. This dataset was fed into our outlined problem statement and constraints, resulting in the convergence of the research multi-objective model. This problem and our model do not converge on any specific result; hence, an outside client chooses the best-fit model for their needs. This research aims to identify multiple optimal configurations that maximize accuracy while minimizing computational cost, providing insights into efficient model training strategies. Results highlight the benefits of adaptive tuning approaches in achieving an optimal balance between performance and resource consumption.
Recommended Citation
Kacir, Alex and Beresford, Ashton, "Multi-Objective Optimization of Hyperparameter Tuning" (2025). ONU Student Research Colloquium. 63.
https://digitalcommons.onu.edu/student_research_colloquium/2025/Posters/63
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Multi-Objective Optimization of Hyperparameter Tuning
ONU McIntosh Center; Activities Room
Hyperparameter tuning is crucial in optimizing deep learning models, often requiring a balance between computational efficiency and model performance. This research explores multi-objective optimization performance for hyperparameter tuning, focusing on the trade-off between compiling time and the resulting model accuracy. To achieve results in this research, we used the Pymoo library, a Python library used for multi-objective optimization, and its documentation in order to amend previously worked-on problems to fit our needs. A custom-made dataset was used where a default configuration of a set image classification model using simple CNNs where hyperparameters are systematically altered upon running the model, recording of the training time and resulting accuracy of the model was done. This dataset was fed into our outlined problem statement and constraints, resulting in the convergence of the research multi-objective model. This problem and our model do not converge on any specific result; hence, an outside client chooses the best-fit model for their needs. This research aims to identify multiple optimal configurations that maximize accuracy while minimizing computational cost, providing insights into efficient model training strategies. Results highlight the benefits of adaptive tuning approaches in achieving an optimal balance between performance and resource consumption.