https://github.com/SimonBlanke/Gradient-Free-Optimizers

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
https://github.com/SimonBlanke/Gradient-Free-Optimizers

bayesian-optimization blackbox-optimization constrained-optimization evolution-strategies gradient-free-optimization hill-climbing hyperactive hyperparameter-optimization machine-learning meta-heuristic nelder-mead optimization particle-swarm-optimization random-search simulated-annealing tree-of-parzen-estimator

Last synced: about 2 months ago
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Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

• Host: GitHub
• URL: https://github.com/SimonBlanke/Gradient-Free-Optimizers
• Owner: SimonBlanke
• License: mit
• Created: 2020-04-04T06:35:55.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-10-21T19:17:48.000Z (2 months ago)
• Last Synced: 2024-10-22T12:11:27.140Z (2 months ago)
• Topics: bayesian-optimization, blackbox-optimization, constrained-optimization, evolution-strategies, gradient-free-optimization, hill-climbing, hyperactive, hyperparameter-optimization, machine-learning, meta-heuristic, nelder-mead, optimization, particle-swarm-optimization, random-search, simulated-annealing, tree-of-parzen-estimator
• Language: Python
• Homepage: https://simonblanke.github.io/gradient-free-optimizers-documentation
• Size: 38.2 MB
• Stars: 1,204
• Watchers: 21
• Forks: 84
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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