Complete Implementation by adding Genetic algorithm

[Da4kek]

Have to rework the algorithm and add a heuristic algorithm for complete implementation.

[vargab100]

I am trying GA with TPOT

"""
tpot= TPOTRegressor(generations= 5, population_size= 24,offspring_size = 12,mutation_rate=0.9,subsample=1.0, n_jobs=1,
verbosity= 2, early_stop= 12,random_state=20,crossover_rate=0.1,
config_dict={'sklearn.ensemble.RandomForestRegressor': param}
)
tpot.fit(X_train,y_train)
"""

is where is a way or any in-build function to print the hyperparameter values for which my model is best fitted???

[Da4kek]

You can use sci-kit learn Pipeline and gridsearchcv.
Pipeline, to combine more than 1 model
Gridsearchcv, to brute force hyperparameters and print the best hyperparameter from the given.
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
here's the documentation in case you want to refer.