Gradient-Free-Optimizers
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Performance in high dimensional search-spaces
The issue #49 showed a slowdown of optimization algorithms in high dimensional (>1000) search-spaces. So this is aiming at improving this performance (for now) in random-search and hill-climbing. If I am successful I can adapt those changes to other optimization algorithms.
This issue does not aim to improve the performance of smbo in high-dim search-spaces. The performance of those algorithms cannot easily be improved by using some numpy arrays here and there.
Hi @SimonBlanke ! Could you assign this issue to me ? I'd like to solve it Thank You !