https://github.com/dingoeatingfuzz/jeans

Genetic Algorithms in javascript
https://github.com/dingoeatingfuzz/jeans

Last synced: 18 days ago
JSON representation

Genetic Algorithms in javascript

• Host: GitHub
• URL: https://github.com/dingoeatingfuzz/jeans
• Owner: DingoEatingFuzz
• Created: 2015-01-06T07:36:05.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2015-01-13T08:07:07.000Z (about 10 years ago)
• Last Synced: 2024-11-08T22:19:18.608Z (2 months ago)
• Language: JavaScript
• Size: 242 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## jeans

Genetic Algorithms in javascript

#### Overview

Genetic algorithms are a class of search, or hill-climbing, algorithms. It searchs through a potential solutions landscape looking for a "good enough" solution. That is, even if there is a *best* answer, we don't care; we are only interested in an answer that meets our requirements.

Check out the examples for more explanation.

#### Features

This is a barebones implementation that supports crossover, mutation, and elitism bias.

#### Details

**Genetic Algorithm**

- `populationSize` - The number of individuals in each generation

- `mutationRate` - A value from 0 - 1 that describes the percent chance an allele will mutate

- `elitismBias` - A value from 0 - 1 that describes the percent of the top population that will be guaranteed parents for the next generation. *When `elitismBias` is 0, all parents are selected based on weighted chance using their fitness score.

- `success` - A function that takes an individual to evaluate if it is successful. That is, it meets the search criteria.

- `mutators` - An object representing how to mutate a chromosome, where the key is the name of the chromosome to mutate, and the value is the mutator function.

- `mutator function` - A function that takes the data for a chromosome and the `mutationRate`

- `spawn` - A function that takes no arguments that creates a new individual. This is called on init n times, where n is the population size

- `reproduce` - A function that takes two arguments, where each argument is an individual

#### Examples

Check out the examples directory for simple usages of genetic algorithms (and jeans).

**Coins** - Find a collection of 100 coins that adds up to $5. For fun, require that one coin be a dollar coin.