# methods/selection.js

``````/**
* Genetic Algorithm Selection Methods (Genetic Operator)
*
* @namespace
*
* @see [Genetic Algorithm - Selection]{@link https://en.wikipedia.org/wiki/Selection_(genetic_algorithm)}
*
* @example
* let { architect, methods } = require("@liquid-carrot/carrot");
*
* let myNetwork = new architect.Perceptron(1,1,1);
* let myTrainingSet = [{ input:[0], output:[1]}, { input:[1], output:[0]}];
*
* myNetwork.evolve(myTrainingSet, {
*  generations: 10,
*  selection: methods.selection.POWER // eg.
* });
*/
const selection = {
/**
* @constant
* @type {object}
* @description [Fitness Proportionate / Roulette Wheel Selection on Wikipedia](https://en.wikipedia.org/wiki/Fitness_proportionate_selection)
* @default
*
* @example
* let { architect, methods } = require("@liquid-carrot/carrot");
*
* let myNetwork = new architect.Perceptron(1,1,1);
* let myTrainingSet = [{ input:[0], output:[1]}, { input:[1], output:[0]}];
*
* myNetwork.evolve(myTrainingSet, {
*  generations: 10,
*  selection: methods.selection.FITNESS_PROPORTIONATE // eg.
* });
*/
FITNESS_PROPORTIONATE: {
name: 'FITNESS_PROPORTIONATE',
},
/**
* @constant
* @type {object}
* @description A random decimal value between 0 and 1 will be generated (e.g. 0.5) then this value will get an exponential value (power, default is 4). So 0.5**4 = 0.0625. This is converted into an index for the array of the current population, sorted from fittest to worst.
* @default
*
* @example
* let { architect, methods } = require("@liquid-carrot/carrot");
*
* let myNetwork = new architect.Perceptron(1,1,1);
* let myTrainingSet = [{ input:[0], output:[1]}, { input:[1], output:[0]}];
*
* myNetwork.evolve(myTrainingSet, {
*  generations: 10,
*  selection: methods.selection.POWER // eg.
* });
*/
POWER: {
name: 'POWER',
power: 4,
},
/**
* @constant
* @type {object}
* @description [Tournament Selection on Wikipedia](https://en.wikipedia.org/wiki/Tournament_selection)
* @default
*
* @example
* let { architect, methods } = require("@liquid-carrot/carrot");
*
* let myNetwork = new architect.Perceptron(1,1,1);
* let myTrainingSet = [{ input:[0], output:[1]}, { input:[1], output:[0]}];
*
* myNetwork.evolve(myTrainingSet, {
*  generations: 10,
*  selection: methods.selection.TOURNAMENT // eg.
* });
*/
TOURNAMENT: {
name: 'TOURNAMENT',
size: 5,
probability: 0.5,
},
};

module.exports = selection;
``````