What's the difference between evolutionary algorithms and MCMC sampling? They seem to both be a matter of:

> take some set of points in a high-dimensional space
> test each point against a fitness/sampling function
> take points with highest values, create a lot of variations on them
> throw other points away
> repeat with the variations

What am I missing? Is MCMC sampling just an evolutionary algorithm for sampling distributions?