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the title mentions two new evolutionary algorithms, and I think it would be good if the title clarifies which two are new. it seems the new ones are the genetic algorithm and differential evolution

i find it interesting that Gradient-Free-Optimizers is used in a library for hyperparameter optimization. so in essence using a gradient-free approach to optimize a gradient-based approach

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The title was to long, but I edited my comment for clarification. Hyperparameter optimization is normally done with gradient-free-optimization techniques, since a gradient cannot be computed. This is opposed to the optimization of parameters during training (e.g. neural network weights).

I was looking for papers on this topic a while back. Here’s one which lists many derivative-free methods:

https://arxiv.org/abs/1904.11585

that's quite the comprehensive paper! it's almost like a mini textbook

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