I am making some stochastic training ensemble classes in Python, and I want to get hyperparameters values. Grid search will take too long for moderate data sets, because in my stochastic training I train different learners on different dropouts, as in cross-validation, then I average or weight-average on best error for dropout. Having this set, I want to get best parameter values at every iteration, so I thought to use a genetic algorithm approach.
I found one example on Github, but failed to install, and I am looking for more references of genetic algorithms for doing this. Can anyone help me?