apply evolution strategies to supervised, classification tasks. at every iteration (“generation”), a population of parameter vectors (“genotypes”) is perturbed (“mutated”) and their objective function value (“fitness”) is evaluated.
es is an optimization technique that learns parameters without backpropagation. no gradients are computed - why do things the easy way when you can do it the hard way?
if you know scikit, you know the drill.
# iris dataset
iris = learn.datasets.load_dataset('iris')
X = iris.data
y = np.eye(3)[iris.target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
# create and fit es model
from Dawkins import Dawkins
d = Dawkins(n_pop=200, n_generations=2000)
d.fit(X_train, y_train)
d.predict(X_test, y_test)