You might be interested in Lagrange interpolation (link only talks about one-variable version). Just input 100 points from your values and you'll probably get something good. You may need more points for it to be accurate at locations of high variance. If you insist on using an evolutionary algorithm, you might use it to choose near-optimal choices of data points, but in many cases you could probably do almost as well by spreading them around evenly.

Two-variable interpolation isn't on Wikipedia. But it is Google-able, and I found it described in sufficient generality in section 3 of this paper. Or, if you have access to MATLAB, a "static" 2D-interpolater has already been coded in this script (I haven't used this one, so it might be unwieldy or otherwise unsuited to your purposes).

Hope this helps you out!

How to approximate unknown two-dimensional function?
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