Comments (4)
Thank you for adding it to EvoJax and sharing your interesting results!
I am particularly pleased to see that CR-FM-NES is performing well across a range of problems, as I have been focused on its practical performance.
Please let me know if there are any improvements needed in the algorithm 😎
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It may not be easy, but tuning learning rates in CR-FM-NES may be advantageous, if the problem is multimodal and/or noisy.
We recently developed learning rate adaptation for CMA-ES (https://arxiv.org/abs/2304.03473; GECCO'23 best paper nominated). Although applying this method to CR-FM-NES may be a bit tedious, I believe simply changing learning rate (i.e. decreasing it by a scalar factor) is effective if the problem is difficult.
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Note that, however, a small learning rate (and learning rate adaptation) may need to take a sufficient evaluation budget. So if the evaluation of the objective function is not cheap, this is not a very attractive option.
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Related Issues (6)
- A simple fix for h_inv overflow with large dimensions HOT 4
- Recommend to add assertion for the value of "lamb" in CRFMNES init HOT 2
- get_h_inv(dim) values gets too big when I try with dim>20000
- get a pdf of a sample for importance sampling HOT 1
- Running example with a high dimension stalls, then progresses again HOT 3
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