Python library for CMA Evolution Strategy.
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Updated
May 30, 2024 - Python
Python library for CMA Evolution Strategy.
(GECCO2023 Best Paper Nomination) CMA-ES with Learning Rate Adaptation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation on C#
A bare-bones Python library for quality diversity optimization.
MoRIS (Model of Routes of Invasive Spread). A simulator of human-mediated dispersal via transportation networks.
Website with interactive client-side CMA-ES (blackbox optimizer) demos. Reinforcement-learning demos allow users to control RL-trained robots.
High-performance Echo State Network simulation, optimization and visualization in modern C++.
Derivative-Free Global Optimization Method (C++, Python binding)
Genetic algorithms and CMA-ES (covariance matrix adaptation evolution strategy) for efficient feature selection
(CEC2022) Fast Moving Natural Evolution Strategy for High-Dimensional Problems
Reproduce the results of "Neuroevolution of Self-Interpretable Agents" paper
Official implementation of the MM'21 paper "Constrained Graphic Layout Generation via Latent Optimization" (LayoutGAN++, CLG-LO, and Layout evaluation)
(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"
(EvoApps2022) "Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies"
Python implementation of Regulated Evolution Strategies with Covariance Matrix Adaption for continuous "Black-Box" optimization problems.
Blackbox feasibility prediction with machine learning to optimize a CMA-ES algorithm
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