Genetic programming is an inefficient search method, and will require many evaluations of the cost function to optimize anything. In the case of DCNNs, evaluating an architecture can keep a modern GPU busy for days, so genetic algorithms are pretty much out of the question.
I think an easy way to improve our models in the short term is to make more of the parameters we use be learnable: the parameters of the non-linearity are a good place to start with, and another would be the parameters of the data augmentation transformations.
One could consider that learned data augmentation schemes implement a form of guided visual attention.
I think an easy way to improve our models in the short term is to make more of the parameters we use be learnable: the parameters of the non-linearity are a good place to start with, and another would be the parameters of the data augmentation transformations.
One could consider that learned data augmentation schemes implement a form of guided visual attention.