Genetic algorithms (GA) is yet another great example how biology can inspire computer science concepts. On a high level it can be though of optimization or a search algorithm.

This blogs gives a great breakdown and a simple implementation of how GA works:

Similar to gradient descent or ascent algorithms widely used in deep learning to train the deep neural networks, GA is heuristic and is based on a Monte Carlo (i.e. random) approach. So in principle there could be a way of training based on GA. In the paper above it seems however, GA was used to narrow down the dimensions of the target (Y): “Targets in our main experiments were constructed using nine dimensions (three for each of the two colours and three for texture), resulting in a parameter space containing a total of 6.18 × 10^17 possible patterns. Since our parameter space was so large, we were unable to exhaustively or randomly select targets with sufficient diversity. Therefore, we implemented a genetic algorithm (GA) to optimise the colour and texture parameters, based on participants’ responses trial by trial, for hardest or easiest to see stimuli”.

This is not unique, in a paper below they seem to use GA for deep reinforcement learning: https://arxiv.org/pdf/1905.04100.pdf