Interestingly, it’s looking more and more like evolution isn’t random, and not only is evolution happy with “good enough”, it seems like it actively stops there
Based on some recent experiments with bacteria and editing out existing genes, it seems like it chooses one genetic area at a time, and once it makes a marginal increase in an area it switches to another
It’s possibly a mechanism to avoid a population boom then bust - if you improve too much too fast, you’ll outcompete your environment to the point you destroy your own ecological niche
However it works (and figuring that out is bleeding edge research), it’s very old. Interestingly, Darwin’s later (unpublished) writings went in this direction, but the theories lost out to the random mutation theory
I’d assume that this is a direct consequence of the impact mutations can have during short spans of generations. The closer you are to a local optimum, the more mutations you need to get into different (albeit better) optima.
Essentially, the step size of the optimisation process is usually too small to make this jump, you need a lot of luck to make it work (since any transitional generations have to stay alive long enough to reproduce and outcompete/find a new niche) - which automatically gives the rest of the ecosystem time to “catch up”, changing the landscape of the fitness function and thus providing new pathways to better optima.
Interestingly, it’s looking more and more like evolution isn’t random, and not only is evolution happy with “good enough”, it seems like it actively stops there
Based on some recent experiments with bacteria and editing out existing genes, it seems like it chooses one genetic area at a time, and once it makes a marginal increase in an area it switches to another
It’s possibly a mechanism to avoid a population boom then bust - if you improve too much too fast, you’ll outcompete your environment to the point you destroy your own ecological niche
However it works (and figuring that out is bleeding edge research), it’s very old. Interestingly, Darwin’s later (unpublished) writings went in this direction, but the theories lost out to the random mutation theory
I’d assume that this is a direct consequence of the impact mutations can have during short spans of generations. The closer you are to a local optimum, the more mutations you need to get into different (albeit better) optima.
Essentially, the step size of the optimisation process is usually too small to make this jump, you need a lot of luck to make it work (since any transitional generations have to stay alive long enough to reproduce and outcompete/find a new niche) - which automatically gives the rest of the ecosystem time to “catch up”, changing the landscape of the fitness function and thus providing new pathways to better optima.