🥕 Evolutionary Neural Networks in JavaScript
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Updated
Jan 7, 2023 - JavaScript
🥕 Evolutionary Neural Networks in JavaScript
Genetic Algorithm for Neural Network Architecture and Hyperparameter Optimization and Neural Network Weight Optimization with Genetic Algorithm
Python implementation of the Semantic Learning Machine
Neuroevolution through Augmenting Topologies
Several approaches using deep reinforcement learning to play Super Mario Bros.
A flexible NEAT-based neural network library for .NET, empowering intelligent agents in research, games, and AI simulations.
Not the typical snake game. The snake no longer needs you - it grows on its own (neuro-evolution at its best)
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
contains code related to all machine learning models being studied.
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.
Heuristic AI for playing Tetris. Uses neuro-evolution algorithm.
C++ ES-HyperNEAT algorithm implementation
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.
An implementation of the NEAT-Algorithm and an UE4 project to try it out.
Various studies show that criticality is an attractor in biological evolution. Which conditions have to be fulfilled, such that criticality acts as an attractor in our neuroevolution simulation? -- Masters Thesis Project ---
Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)
The power of Neural Networks and neuro-evolution. Creating and training digital creatures to find the path to a target.
I'm learning about machine learning algorithms by implementing them and using them in Java.
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.
Developing an Intelligent Agent from scratch to play a game with Applying Neuroevolution to achieve high scores
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