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I would love to see this with a more complicated search space. The two examples are too easy to climb.

Would be really interesting to see how the different algorithms travel through a landscape where it is hard to find the optimum.

And to make it perfect: Put information about the performance below each visualization.

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This article is the first of a series of articles about the subject. The next article will be about solving more difficult problems with these methods. Stay tuned-

Near the bottom he does show the results of running it on 100-D Rastrigin (and mnist before that) with stats and plots. Or do you mean something more complex even than that?

Well, the point of the guide is the nice 2-D visualization. Which he cannot do for the 100-D problem of course. It would be nice to make the same guide but with a harder 2-D landscape.

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