Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
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Updated
Sep 25, 2024 - Python
Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
Three implemented evolutionary strategies using DEAP to optimize energy scheduling tasks.
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
Website with interactive client-side CMA-ES (blackbox optimizer) demos. Reinforcement-learning demos allow users to control RL-trained robots.
This repository contains an improvement for any covariance-matix-adaptation-like evolution strategy exploiting gradient or its estimation
The official repo for GECCO 2022 paper High-Performance Evolutionary Algorithms for Online Neuronal Control in vivo and in silico
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"
Evolutionary & genetic algorithms for Julia
Python library for stochastic numerical optimization
ESKit is a portable library written in C, that provides implementations of some self-adaptive evolution strategies
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
All code for the results and figures shown in the report for the course AE4350.
Self-Interpretable Agent implemented on the Procgen game 'Dodgeball'.
Bandit and Evolutionary Algorithms using Python
CMA-ES in MATLAB
A fully decentralized hyperparameter optimization framework
Unity In Editor Deep Learning Tools. Using KerasSharp, TensorflowSharp, Unity MLAgent. In-Editor training and no python needed.
StochANNPy (STOCHAstic Artificial Neural Network for PYthon) provides user-friendly routines compatible with Scikit-Learn for stochastic learning.
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