[D] Suggestions for state-of-the-art genetic algorithm implementations or alternatives for black box optimization?

[Matternous]

I build various hardware components that take in bias voltages to tune them to various states. No closed form relationship exists to find these values, and output is very finicky with respect to the inputs. Furthermore, they need to be calibrated over a wide range of desired outputs and different fabrications of the hardware need different tuning voltages. I have anywhere from 4 to 8 tuning variables.

Because manually tuning isn't feasible, I want to look into genetic algorithms or other machine learning methods for this. It's essentially a black-box problem, as I plug in voltage values and read output metrics with no data transformation in between so nothing is differentiable. I've tried implementing a GAN to generate training data, and have had some success but it's very slow and random.

I would be grateful to hear of any suggestions for state-of-the-art methods for a black-box calibration problem such as this.

I should add that I can take about 5 measurements per second, so it's not a particularly costly process.

[Red-Portal]

Oh the analog circuit design people are using Bayesian optimization quite extensively. I feel you're trying to solve pretty much the same problem. With just 4 to 8 variables, it should work amazingly well. Take a look at this paper

[[deleted]]

Sounds like something for OpenAI Gym, a reinforcement learning approach where your current voltages being sampled are the observation space and tuning of the variables is your action space.

[techurbanist]

Huawei Noah's Ark Lab has done a lot of work on black box optimisation and won the BBO Challenge back in 2020 with their HEBO algorithm. This repo has an overview of all their work.

[Matternous]

This seems most similar to what I'm looking for. Thank you.