Python implementation of the NEAT neuroevolution algorithm
-
Updated
May 23, 2024 - Python
Python implementation of the NEAT neuroevolution algorithm
Evolutionary Algorithm using Python, 莫烦Python 中文AI教学
Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
Accelerated Quality-Diversity
Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
Neuroevolution Framework for Tensorflow 2.x focusing on modularity and high-performance. Preimplements NEAT, DeepNEAT, CoDeepNEAT
TensorFlow Eager implementation of NEAT and Adaptive HyperNEAT
Pure Python Library for ES-HyperNEAT. Contains implementations of HyperNEAT and ES-HyperNEAT.
A pytorch implementation of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm
This program evolves an AI using the NEAT algorithm to play Super Mario Bros.
A public python implementation of the DeepHyperNEAT system for evolving neural networks. Developed by Felix Sosa and Kenneth Stanley. See paper here: https://eplex.cs.ucf.edu/papers/sosa_ugrad_report18.pdf
NeuroEvolution Optimization with Reinforcement Learning
Genetic learning algorithm implementation for simulations, games, or general machine learning problems
Python implementation of the NEAT neuroevolution algorithm
An implementation of CoDeepNEAT using pytorch with extensions
Neuroevolution framework for Python.
AI research environment for program generation.
Stock trading based on MACD indicator, using NEAT and naive algorithm
Add a description, image, and links to the neuroevolution topic page so that developers can more easily learn about it.
To associate your repository with the neuroevolution topic, visit your repo's landing page and select "manage topics."