I wrote my MSc on improving certain Genetic Algorithms by incorporating biologists' ideas of "exaptation," or having features that were evolved for use in one context providing a jump in another context.
For example, it's quite difficult to imagine how wings evolved, since 1% of a wing seems to offer no fitness advantage, and yet you can't evolve 10% of a wing without evolving 1%. One idea from biology is that feathers probably evolved first as thermoregulators (basically good fur for dinosaurs), and small wing flaps may have evolved similarly to keep animals warm, and only once they had evolved large enough could they be used in a "flying squirrel" way to get from tree to tree, and at that point you're in a different fitness landscape and can start evolving for wings proper.
It turns out such an idea may be useful for evolving hard-to-evolve neural networks and the like. (Although this was 15 years ago, and genetic algorithms have fallen out of fashion.)
For example, it's quite difficult to imagine how wings evolved, since 1% of a wing seems to offer no fitness advantage, and yet you can't evolve 10% of a wing without evolving 1%. One idea from biology is that feathers probably evolved first as thermoregulators (basically good fur for dinosaurs), and small wing flaps may have evolved similarly to keep animals warm, and only once they had evolved large enough could they be used in a "flying squirrel" way to get from tree to tree, and at that point you're in a different fitness landscape and can start evolving for wings proper.
It turns out such an idea may be useful for evolving hard-to-evolve neural networks and the like. (Although this was 15 years ago, and genetic algorithms have fallen out of fashion.)