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Bacterial genomes are much, much simpler than animal genomes. They reproduce very quickly, and even if many bacteria suffer from a problematic (or fatal) mutati

Bacterial genomes are much, much simpler than animal genomes. They reproduce very quickly, and even if many bacteria suffer from a problematic (or fatal) mutation, others will be OK, and some may even have adaptations to changing conditions. Animals have much more complex genomes. They are more at risk from mutations, and they cannot reproduce as quickly. There is more incentive to "get it right", if you will. With asexual reproduction, selection occurs at the level of the genome, meaning you are as good as your "worst" gene. You can't get rid of harmful mutations, and it seems like (complex) species that get stuck in asexual reproduction go extinct. With sexual reproduction, selection can occur at the level of the gene, allowing beneficial mutations or recombinations to spread through a population AND correcting (many) harmful mutations.

spacehacker 10y ago
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

spacehacker 10y ago

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