If you define machine learning algorithms as those that learn from data, then okay. In that case, EAs are reinforcement learning algorithms and other methods like Q-learning are also not ML algorithms.
However, I use the canonical definition by Mitchell: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.[1] In this case, it's clear that in fact EAs are very much so an ML method. One could even go so far as to say they are more appropriately ML than things like SVMs as they are truly learning from experience rather than just data being handed to them.
[1] http://en.wikipedia.org/wiki/Machine_learning#Definition