https://github.com/corymccartan/genetic
Basic genetic optimization algorithm, written in JavaScript.
https://github.com/corymccartan/genetic
Last synced: 4 months ago
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Basic genetic optimization algorithm, written in JavaScript.
- Host: GitHub
- URL: https://github.com/corymccartan/genetic
- Owner: CoryMcCartan
- Created: 2016-03-29T19:41:52.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2016-04-12T00:53:57.000Z (about 9 years ago)
- Last Synced: 2025-02-11T01:56:22.753Z (4 months ago)
- Language: JavaScript
- Homepage:
- Size: 7.81 KB
- Stars: 3
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# JSGA
## JavaScript Genetic Algorithm
JSGA is an ES6 genetic algorithm library.
## Installation
- Bower: `bower install jsga`
Or you can download `jsga.js` above.
## Usage
JSGA operates on chromosomes --- lists of numbers. It generates successive
generations of chromosomes by mixing the chromosomes of randomly selected parents,
and by randomly mutating chromosomes. It evaluates each individual by using a
user-provided fitness function. Only the fittest individuals from each generation
are carried over to the next.
Start by creating a JSGA object:
```javascript
let algorithm = JSGA({
length: 10,
radix: 10,
fitness: function(individual) {
return individual.reduce((p, c) => p + c); // the fitness is the sum of all the values
},
size: 100,
children: 4,
mutationRate: 0.05,
crossovers: 1
});
```
Notes on the parameters:
- `length` is the length of the chromosome.
- `radix` is the 'base' used to encode the data. Each number in the chromosome
will have a range from `0` to `radix`.
- `fitness` is the fitness function used to evaluate individuals. It is passed
a chromosome (array of numbers) and expects a number to be returned. JSGA
will try to maximize the fitness function.
- `size` is the number of individuals in the population. This must be an even number.
- `children` is the number of offsping each pair of parents will produce. The
default is 4.
- `mutationRate` is the rate of random mutations, in mutations/number. That
is, for the default mutation rate of 0.05, around one in every twenty numbers
in each chromosome will be randomly mutated.
- `crossovers` is the number of times to randomly cross over the parents
chromosomes. This is the primary source of variation. The default is 1.
To run the algorithm, simply call the `run` method with the number of
generations to run the algorithm. Pass `-1` to make the algorithm run forever.
`run` is a generator which yields an object containing information about the
current generation.
```javascript
for (let generation of algorithm.run(50)) {
console.log(`Generation ${generation.generation}`);
console.log(`Array of individuals: ${generation.population}`);
console.log(`Best individual: ${generation.best.params}`);
console.log(`Best individual's fitness: ${generation.best.fitness}`);
}
```