Neuroevolution: A different kind of deep learning

In other words, neuroevolution seeks to develop the means of evolving neural networks through evolutionary algorithms. A common approach to such weight shifting is called stochastic gradient descent, which is the aforementioned formula popular throughout deep learning. At the time, its small group of practitioners thought it might be an alternative to the more conventional ANN training algorithm called backpropagation (a form of stochastic gradient descent). In these early systems, neuroevolution researchers would (as in deep learning today) decide on the neural architecture themselves--which neurons connect to which--and simply allow evolution to decide the weights instead of using stochastic gradient descent.