This repository contains the code for our final group project on Bayesian Optimization. We adapted the original DAMM hackathon problem to use Optuna (Tree-structured Parzen Estimator, TPE) for hyperparameter tuning of a Genetic Algorithm.

• ga_optimizer.py: The Genetic Algorithm with hyperparameters exposed for tuning (mut_prob, elitism, tour_size, w_cap, w_inc, w_urg, pop_size, n_gen).
• optuna_optimizer.py: The Optuna study setup that optimizes the GA parameters.
• app.py: A Streamlit dashboard where you can see the Bayesian network of changeovers AND run the Optuna Hyperparameter Tuning dynamically on the GA.

pip install -r requirements.txt
streamlit run app.py