The dropout regularization method for neural networks was sort of inspired by this observation, i.e. that recombination (combining genes of more than one indivi

The dropout regularization method for neural networks was sort of inspired by this observation, i.e. that recombination (combining genes of more than one individual) makes the resulting structures more robust [1]. The idea is basically that it avoids overfitting to the evolutionary context. This has two effects: the fitness cannot change very quickly in response to single mutations, and, conversely, it avoids that genes co-adapt such that the entire genome becomes inflexible at exploring alternatives. To achieve the same without sex, mutations would would have both break up the co-adapted genes without breaking their functions and also find a good alternative mutation. With sex, it does not matter if co-adapted genes are broken up, because they are forced to evolve to deal with it. (This is kind of similar to the concept of learning to learn in neural network research.) Also, if a parent gene is unfit, it is possible that the child can escape this mutation by receiving that part of the genome from the other parent instead by a 50% chance. [1] https://arxiv.org/abs/1207.0580 https://arxiv.org/abs/1207.0580