Luckily machine learning came along and has shown empirically that the solution is to increase the number of dimensions. Our tiny neural nets of the 90s also got stuck in local maxima. But when we could finally make huge and deep networks, the problem more or less disappeared.
I believe this is the real solution to the local maxima problem in evolution. Real evolution works with a massive number of parameters, which means exploration of the problem space doesn't get stuck in local maxima, just as it doesn't in Deep Learning. When the number of tweakable parameters outstrips the dimensionality of the problem space then there is always some angle pointing up out of the valleys.
This is a different, and I think more general way of expressing the typical counterargument which you explained. "There is always some angle pointing up" is a more general way of saying "just one mutation is usually enough to create a functional prototype".