NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Evolutionary & genetic algorithms for Julia
A fully decentralized hyperparameter optimization framework
Python library for stochastic numerical optimization
Unity In Editor Deep Learning Tools. Using KerasSharp, TensorflowSharp, Unity MLAgent. In-Editor training and no python needed.
CMA-ES in MATLAB
StochOptim provides user friendly functions to solve optimization problems using stochastic algorithms
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
The official repo for GECCO 2022 paper High-Performance Evolutionary Algorithms for Online Neuronal Control in vivo and in silico
StochANNPy (STOCHAstic Artificial Neural Network for PYthon) provides user-friendly routines compatible with Scikit-Learn for stochastic learning.
Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
Evolutionary algorithms from the papers
StochOPy WebApp is hosted online at
This github repository contains the official code for the papers, "Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses" and "One Pixel Attack for Fooling Deep Neural Networks"
Website with interactive client-side CMA-ES (blackbox optimizer) demos. Reinforcement-learning demos allow users to control RL-trained robots.
Bandit and Evolutionary Algorithms using Python
Self-Interpretable Agent implemented on the Procgen game 'Dodgeball'.
Python framework for black-box optimization.
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