Genetic Lab




A lightweight genetic algorithm library.

$3

- Added support for custom configuration, ex. custom randomizer function

For a more detailed list of updates, see CHANGELOG.

Getting Started

$3

Add to existing project using npm or yarn.

``
npm install genetic-lab --save
`

`
yarn add genetic-lab
`

$3

Import the library to your project.

`js
const GeneticLab = require("genetic-lab");

// using ES6 import
// import GeneticLab from 'genetic-lab';

const { Selection, Mutation, Crossover } = GeneticLab;

const {
createRoulleteWheelSelection,
createStochasticUniversalSampling,
createRankSelection,
createTournamentSelection
} = Selection;

const { pointCrossover, uniquePointCrossover } = Crossover;

const { mutateOrder, createMutation } = Mutation;
`

Check the examples for additional reference on using this library.

Fitness

A fitness function should check the fitness of a single individual and return a number based on it's fitness score.

For example:

`js
// A fitness function that optimizes for highest binary string value
function getFitness(individual) {
return parseInt(individual, 2);
}

getFitness("010111"); // => 23
`

Selection

$3

Creates a selection function that uses _Roullete Wheel_ method.

`js
const population = ["000", "111"];
const selection = createRoulleteWheelSelection(getFitness);
selection(population); // => ['111']
`

#### Config (Optional)
- randomizer?: () => number; // function that replaces Math.random()

$3

Creates a selection function that uses _Stochastic Universal Sampling_ method. Number of individuals selected is based on count.

`js
const population = ["000", "111", "101"];
const selection = createStochasticUniversalSampling(getFitness, { count: 2 });
selection(population); // => ['111', '101'] or ['101', '111']
`

#### Config
- randomizer?: () => number; // function that replaces Math.random()
- count: number;

$3

Creates a selection function that uses _Rank Selection_ method. Number of individuals selected is based on count.

`js
const population = ["000", "111", "101"];
const selection = createRankSelection(getFitness, { count: 2 });
selection(population); // => ['111', '101']
`

#### Config
- randomizer?: () => number; // function that replaces Math.random()
- count: number;

$3

Creates a selection function that uses _Tournament Selection_ method. Size of tournament is dependent on tournametSize.

`js
const population = ["000", "111", "101"];
const selection = createTournamentSelection(getFitness, 2);
selection(population); // => ['111'] or ['101']
`

#### Config (Optional)
- randomizer?: () => number; // function that replaces Math.random()

Mutation

$3

Swaps two genes randomly.

`js
mutateOrder("ABC"); // => 'BAC' or 'CBA' or 'BCA'
mutateOrder("10"); // => '01'
`

$3

Creates a mutation function that randomly changes a gene based on sample.

`js
const sample = ["1", "0"];
const mutateBinary = createMutation(sample);
mutateBinary("10"); // => '11' or '00'
`

Crossover

$3

Creates two offspring based on _parentA_ and _parentB_ using _index_ as point of crossover. If no index is provided, it would be assigned randomly.

`js
const pair = ["ABCD", "DCBA"];
pointCrossover(...pair, 2); // => ['ABBA', 'DCCD']
`

$3

Creates two unique chromosome offsprings based on unique chromosomes of _parentA_ and _parentB_ using _index_ as point of crossover. If no index is provided, it would be assigned randomly.

`js
const pair = ["ABCD", "DCBA"];
uniquePointCrossover(...pair, 2); // => ['ABDC', 'DCAB']
``

License

This project is licensed under the MIT License - see the LICENSE file for details.