The same fate would accrue to any human planner who tried making random point mutations to their strategies and waiting 768 iterations of testing to adopt a 3% improvement.
The counterargument here is that it might be worth doing if each iteration of testing is cheap enough. With enough computing power, one might very well be able to do that much simulation cheaply and quickly. (One reason genetic algorithms work is that they’re simple enough that we understand how they work, even if their outputs are unpredictable. We aren’t capable of modeling human creativity yet, so throwing enough computing power at a much dumber algorithm that we know works will still give amazingly good results.)
The same fate would accrue to any human planner who tried making random point mutations to their strategies and waiting 768 iterations of testing to adopt a 3% improvement.
The counterargument here is that it might be worth doing if each iteration of testing is cheap enough. With enough computing power, one might very well be able to do that much simulation cheaply and quickly. (One reason genetic algorithms work is that they’re simple enough that we understand how they work, even if their outputs are unpredictable. We aren’t capable of modeling human creativity yet, so throwing enough computing power at a much dumber algorithm that we know works will still give amazingly good results.)