39 skills found · Page 1 of 2
anyoptimization / PymooNSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
CyberAgentAILab / CmaesPython library for CMA Evolution Strategy.
zuoxingdong / Lagomlagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
wildart / Evolutionary.jlEvolutionary & genetic algorithms for Julia
AIworx-Labs / ChocolateA fully decentralized hyperparameter optimization framework
ZhichengHuang / CMAEThe official implementation of CMAE https://arxiv.org/abs/2207.13532 and https://ieeexplore.ieee.org/document/10330745
keurfonluu / StochopyPython library for stochastic numerical optimization
CMA-ES / C CmaesCMA-ES written in ANSI C (yet fairly object-oriented)
tcmxx / UnityTensorflowKerasUnity In Editor Deep Learning Tools. Using KerasSharp, TensorflowSharp, Unity MLAgent. In-Editor training and no python needed.
pengowen123 / CmaesA Rust implementation of the CMA-ES optimization algorithm.
bajeluk / Surrogate CmaesSurrogate CMA-ES (S-CMA-ES and DTS-CMA-ES) is a surrogate-based optimizing evolution strategy. It is based on the N. Hansen's CMA-ES algorithm which is interconnected with Gaussian processes (or random forests, that are, however, not maintained here anymore).
IOHprofiler / ModularCMAESNo description available
akimotolab / CMAES TutorialNo description available
smkalami / Ypea108 Cma EsCMA-ES in MATLAB
yn-cloud / CMAES.NETCovariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation on C#
m1nhengChen / CMAES Reg[ISBI 2025] Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for 2D/3D registration
keurfonluu / StochOptimStochOptim provides user friendly functions to solve optimization problems using stochastic algorithms
jakobbossek / CmaesrCovariance Matrix Adaption - Evolutionary Strategy in R.
Animadversio / ActMax Optimizer DevThe official repo for GECCO 2022 paper High-Performance Evolutionary Algorithms for Online Neuronal Control in vivo and in silico
tp5uiuc / Soft Systems CourseCourse on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
