Faster than gradient descent is not a selective pressure, at least if we’re considering typical ML training procedures. What is a selective pressure is regularization, which functions much more like a complexity prior than a speed prior.
So (again sticking to modern day ML as an example, if you have something else in mind that would be interesting) of course there will be a cutoff in terms of speed, excluding all algorithms that don’t fit into the neural net. But among algorithms that fit into the NN, the penalty on their speed will be entirely explainable as a consequence of regularization that e.g. favors circuits that depend on fewer parameters, and would therefore be faster after some optimization steps.
If examples of successful parameters were sparse and tended to just barely fit into the NN, then this speed cutoff will be very important. But in the present day we see that good parameters tend to be pretty thick on the ground, and you can fairly smoothly move around in parameter space to make different tradeoffs.
Faster than gradient descent is not a selective pressure, at least if we’re considering typical ML training procedures. What is a selective pressure is regularization, which functions much more like a complexity prior than a speed prior.
So (again sticking to modern day ML as an example, if you have something else in mind that would be interesting) of course there will be a cutoff in terms of speed, excluding all algorithms that don’t fit into the neural net. But among algorithms that fit into the NN, the penalty on their speed will be entirely explainable as a consequence of regularization that e.g. favors circuits that depend on fewer parameters, and would therefore be faster after some optimization steps.
If examples of successful parameters were sparse and tended to just barely fit into the NN, then this speed cutoff will be very important. But in the present day we see that good parameters tend to be pretty thick on the ground, and you can fairly smoothly move around in parameter space to make different tradeoffs.