https://github.com/romainsimon/neuroevolution
:hatching_chick: NeuroEvolution of Augmenting Topologies (NEAT) implementation with Tensorflow.js (still WIP)
https://github.com/romainsimon/neuroevolution
genetic-algorithm neat tensorflowjs
Last synced: 4 months ago
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:hatching_chick: NeuroEvolution of Augmenting Topologies (NEAT) implementation with Tensorflow.js (still WIP)
- Host: GitHub
- URL: https://github.com/romainsimon/neuroevolution
- Owner: romainsimon
- License: mit
- Created: 2018-11-11T17:02:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-24T18:03:36.000Z (almost 6 years ago)
- Last Synced: 2025-02-04T00:07:44.669Z (4 months ago)
- Topics: genetic-algorithm, neat, tensorflowjs
- Language: JavaScript
- Homepage:
- Size: 57.6 KB
- Stars: 18
- Watchers: 2
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
NeuroEvolution of Augmenting Topologies (NEAT)
:hatching_chick: Evolving a population of Neural Networks using Tensorflow.js and Genetic Algorithm in ES6. This is an implementation of NEAT (Neuro Evolution of Augmenting topologies)
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [1. Initialize a new population](#1-initialize-a-new-population)
- [2. Evolve the population](#2-evolve-the-population)
- [Examples](#examples)
- [Xor](#xor)
- [License](#license)
## Installation
Install using [`npm`](https://www.npmjs.com/package/neuroevolution):
```bash
npm install neuroevolution
```
or Yarn [`yarn`](https://yarnpkg.com/en/package/neuroevolution):
```bash
yarn add neuroevolution
```
## Usage
### 1. Initialize a new population
The first step should be to initialize a new population.
A population accepts 3 main parameters:
- `populationSize` Total size of the genomes population
- `nbInput` Number of input nodes
- `nbOutput` Number of output node
```javascript
const { Population } = require('neuroevolution')
const population = new Population(100, 2, 4)
```
This will create a population of 100 neural networks with 2 inputs and 4 outputs.
### 2. Evolve the population
You can start evolving the population using `evolve` with 2 parameters :
- `iterations` How many iterations to evolve
- `fitnessFunction` You fitness function that has access to your genome
```javascript
population.evolve(40, genome => {
const network = genome.generateNetwork()
const prediction = network.predict(input)
// ... return a fitness score according to the accuracy of prediction
})
```
This will evolve the population, keeping the fittest neural networks according to your
fitness function accross 40 generations. The number of iterations is cumulative, this means
if your population current generation is 12, evolving with 40 iterations will transform the
population to generation 52.
## Examples
### Xor
To run this example :
```bash
node examples/xor
```
Sample code :
```javascript
const { Population } = require('neuroevolution')
const population = new Population(50, 2, 1, false)
const xor = [
[[0, 0], 0],
[[0, 1], 1],
[[1, 0], 1],
[[1, 1], 0],
]
population.evolve(1000, genome => {
const network = genome.generateNetwork()
let error = 0;
for (const [input, output] of xor) {
const [prediction] = network.predict(input)
error += Math.abs(prediction - output)
}
return 1 - (error / xor.length)
})
```
## License
Neuroevolution is [MIT licensed](./LICENSE).