Exercises

1. Try to run an experiment with different values of the random seed generator that can be changed in line 101 of the retina_experiment.py script. See if you can find successful solutions with other values.
2. Try to increase the initial population size to 1,000 by adjusting the value of the params.PopulationSize hyperparameter. How did this affect the performance of the algorithm?
3. Try to change the number of activation function types used during the evolution by setting the probability of its selection to 0. It's especially interesting to see what happens when you exclude the ActivationFunction_SignedGauss_Prob and ActivationFunction_SignedStep_Prob activation types from selection.