Genetic algorithm for SVM

Hello! I used the sklearn genetic algorithm on random forest to find the best parameters:

genPar = {'num_estimators': Integer(100, 500), 'max_depth': Integer(8, 128),
          'max_samples': Continuous(0.25, 0.9)}

It took about one day to complete and it was great; but when I apply it on Support Vector Machines it takes much more time:

genPar = {'kernel': Categorical(['linear', 'rbf']), 'C': Integer(1, 10),
          'gamma': Integer(1, 10)}

Do you have any suggestion to speed-up the process?

This question seems to be scikit-learn - specific so you might want to post it into their discussion board or GitHub repository.