Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
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Updated
Dec 19, 2017 - JavaScript
Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
GeneticSharp is a fast, extensible, multi-platform and multithreading C# Genetic Algorithm library that simplifies the development of applications using Genetic Algorithms (GAs).
source code from the book Genetic Algorithms with Python by Clinton Sheppard
Using artificial intelligence and genetic algorithms to automatically write programs. Tutorial: http://www.primaryobjects.com/cms/article149
Evolving version of Conway’s Game of Life.
A collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating)
PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially their *Large-Scale* versions/variants (evolutionary algorithms/swarm-based optimizers/pattern search/...). [https://pypop.rtfd.io/]
Source Code for the Book Classic Computer Science Problems in Swift
A 2D/3D visualization of the Traveling Salesman Problem main heuristics
Genetic algorithm for unsupervised machine learning in Go.
A (still growing) paper list of Evolutionary Computation (EC) published in some (rather all) top-tier (and also EC-focused) journals and conferences. For EC-focused publications, only Parallel/Distributed EC are covered in the current version.
Evolutionary Algorithms Framework
A pytorch implementation of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm
Twitter Genetic Algorithm Imagery
AI research environment for the game of Snake 🐍 .
This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.
Hyperparameter tuning for machine learning models using a distributed genetic algorithm
Evolve complex cellular automata with a genetic algorithm.
Python implementation for TSP using Genetic Algorithms, Simulated Annealing, PSO (Particle Swarm Optimization), Dynamic Programming, Brute Force, Greedy and Divide and Conquer
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