Experimental Global Optimization Algorithm
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Updated
Jan 29, 2018 - Python
Experimental Global Optimization Algorithm
Application for global optimization of multiextremal nondifferentiable functions.
Projet d'optimisation, M2 AIC 2018-2019
rsmac - use SMAC to optimize functions in R
Library for global optimization of multiextremal nondifferentiable functions.
Implementation of Reinforcement Algorithms from scratch
A constraint optimizer based intended for noisy black-box functions
Black-box optimizer submitted to BBO challenge at NeurIPS 2020
Surrogate model library for Derivative-Free Optimization
Framework for Black-Box-Optimization of Machine Learning and Neural Network hyper-parameters.
Fair Classification with Gaussian Process (FCGP)
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Blackbox feasibility prediction with machine learning to optimize a CMA-ES algorithm
Python implementation of the Active-Set (1+1)-ES
A library for the hyperparameter optimization of deep neural networks
Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。
Hard Test Problems for testing evolutionary algorithms.
Serial and Parallel Codes for the Global Optimization Algorithm DIRECT
Python implementation of Regulated Evolution Strategies with Covariance Matrix Adaption for continuous "Black-Box" optimization problems.
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