Python packages for particle swarms, genetic algorithms. Scikit-opt maybe? [D]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
I'm working with a client on a curve-fitting optimization problem. They are currently using a constrained Levenburg-Marquardt optimizer for their task which is complex, slow, and sometimes gets stuck in local minima.
I suggested using particle swarm optimization (PSO), and the client suggested genetic algorithms (GA). I would like to compare the existing method to at least these two other options. For this first phase, I don't need to worry about speed or GPU-friendliness. I would like data visualization to be easy.
I have experience with scikit-learn, and I just discovered scikit-opt. I have also found several other packages which implement only PSO, or only GA.
Is anyone out there using scikit-opt? What do you think of it? If you have used other PSO or GA packages, what do you think of those?
Thanks for any advice you may have.
[link] [comments]
More from r/MachineLearning
-
For the people who got reviews back from neurips, cvpr, eccv, etc and also tested their paper through an agentic reviewer like the stanford one, how different were the reviews? [D]
Aug 14
-
Building text to ASCII diffusion model , need advice and guidance [P]
Aug 14
-
A collision-entropy floor for watermark/retrieval AI-text detection. Looking for a sanity check before I take this further [D]
Aug 14
-
Are supervised and unsupervised learning still relevant today? [D]
Aug 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.