🥕 Evolutionary Neural Networks in JavaScript

🦖 A fast & simple neuro-evolution library for Python

Genetic Algorithm for Neural Network Architecture and Hyperparameter Optimization and Neural Network Weight Optimization with Genetic Algorithm

sNEAT: A simplified implementation of NEAT (Neuro-Evolution of Augmenting Topologies) in Python

Several approaches using deep reinforcement learning to play Super Mario Bros.

Genetic algorithm (NEAT) to generate and optimize deep neural networks for the game "Flappy Bird".

C++ ES-HyperNEAT algorithm implementation

Developing an Intelligent Agent from scratch to play a game with Applying Neuroevolution to achieve high scores

Machine learning model that learns to play Flappy bird game developed using Neural networks and Genetic algorithm(NEAT).

I'm learning about machine learning algorithms by implementing them and using them in Java.

Which dynamical regime is beneficial for biological systems in the context of the criticality hypothesis? Agent-based evolutionary foraging game with experiments to evaluate generalizability, ability to perform complex tasks and evolvability of agents with respect to their dynamical regime. Paper: https://arxiv.org/abs/2103.12184

neuro-evolution applied to the game of rock paper scissors

Heuristic AI for playing Tetris. Uses neuro-evolution algorithm.

Python implementation of the Semantic Learning Machine

Evolução Neural aplicada ao jogo Flappy Bird.

An implementation of the NEAT-Algorithm and an UE4 project to try it out.

Paper: https://doi.org/10.1162/isal_a_00412 Which dynamical regime is beneficial for biological systems? Agent-based evolutionary foraging game with experiments to evaluate generalizability, ability to perform complex tasks and evolvability.

A flexible NEAT-based neural network library for .NET, empowering intelligent agents in research, games, and AI simulations.

The project aims to teach a neural net how to play the famous game 'Flappy Bird'. To play the game deep learning and genetic algorithms are applied.

Implemented Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks from scratch in Python and used ResNet-34 as a feature extractor. Evaluated and compared the classification accuracy of the two networks on the CIFAR-10 dataset.