Python library for CMA Evolution Strategy.
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Updated
Sep 11, 2024 - Python
Python library for CMA Evolution Strategy.
A bare-bones Python library for quality diversity optimization.
Official implementation of the MM'21 paper "Constrained Graphic Layout Generation via Latent Optimization" (LayoutGAN++, CLG-LO, and Layout evaluation)
Distributed implementation of popular evolutionary methods
(GECCO 2022) CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization
Official implementation of "Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning"
(CEC2022) Fast Moving Natural Evolution Strategy for High-Dimensional Problems
Deep learning and evolutionary algorithms for identification of aerodynamic parameters
Reproduce the results of "Neuroevolution of Self-Interpretable Agents" paper
A new version of world models using Echo-state networks and random weight-fixed CNNs
A universal supervisor controller and ER suite for Webots that can be adapted to any wheeled robot morphology with ease. The project is also setup to allow for easy Reinforcement Learning experimentation with some select algorithms (CMA-ES, Novlty Search, MAP-Elites) and neural networks (fixed and recurrent).
(GECCO2023 Best Paper Nomination) CMA-ES with Learning Rate Adaptation
Python implementation of Regulated Evolution Strategies with Covariance Matrix Adaption for continuous "Black-Box" optimization problems.
(EvoApps2022) "Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies"
Convert images into low poly, using an optimizer
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