Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
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Updated
Nov 26, 2025 - Python
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
[JMLR (CCF-A)] PyPop7: A Pure-PYthon LibrarY for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* algorithm variants (from evolutionary computation, swarm intelligence, statistics, operations research, machine learning, mathematical optimization, meta-heuristics, auto-control etc.). [https://jmlr.org/papers/v25/23-0386.html]
Elo ratings for global black box derivative-free optimizers
Sampling based Model Predictive Control package for Model-Based RL research
A simple implementation of SPSA with automatic learning rate tuning
Python library for root-finding in one dimension
Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling
Modular optimization library for PyTorch (work-in-progress).
OMADS - A blackbox optimization python package
This repository contains the official PyTorch implementation of the paper: "Learning Discrete Structured VAE using NES".
Nevergrad Optimizer Benchmarking for 3D Performance Capture
Myopic and non-myopic Global Optimization via IDW and RBF surrogate models
deforce: Derivative-Free Algorithms for Optimizing Cascade Forward Neural Networks
Object-Orientated Derivative-Free Optimisation
Statistical learning models library for blackbox optimization
Trust-Region Filter/Funnel (TRF) solver is developed using the concepts from nonlinear optimisation, derivative-free optimisation and surrogate modelling, and is used to optimise grey box optimisation problems (coupling glass box mathematical models with available derivative information and the black box models without derivative information).
A reimplementation of the derivative-free global optimization algorithm GLIS adjusted for the needs of the team
Blockwise Direct Search (Python version)
🚀 Benchmark GPU and CPU performance accurately across diverse hardware using PyTorch and TensorFlow, generating metrics and dashboards for optimization.
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