[nabla9]

Also. Neural networks commonly use dropout regularization. In dropout your train only fraction (typically 50%) of randomly selected neurons. Effectively creating essembles.

Gradient descent and evolutionary algorithms (and many other search algorithms) advance in the hypothesis space with incremental (stochastic) steps and both algorithms are path dependent. How they generate and update their hypothesis, how big steps they take, how they represent their state, and how they apply randomness creates unique learning bias but there is nothing fundamentally different.