genetic-js-no-ww/test/string.js

Advanced genetic and evolutionary algorithm library (no web workers fork)

var Genetic = require("../lib/genetic");
var assert = require("assert");


var genetic;

beforeEach(function () {
	genetic = Genetic.create();
	genetic.optimize = Genetic.Optimize.Maximize;
	genetic.seed = function() {

		function randomString(len) {
			var text = "";
			var charset = "abcdefghijklmnopqrstuvwxyz";
			for(var i=0;i<len;i++)
				text += charset.charAt(Math.floor(Math.random() * charset.length));
			
			return text;
		}
		
		// create random strings that are equal in length to solution
		return randomString(this.userData["solution"].length);
	};
	genetic.mutate = function(entity) {
		
		function replaceAt(str, index, character) {
			return str.substr(0, index) + character + str.substr(index+character.length);
		}
		
		// chromosomal drift
		var i = Math.floor(Math.random()*entity.length)		
		return replaceAt(entity, i, String.fromCharCode(entity.charCodeAt(i) + (Math.floor(Math.random()*2) ? 1 : -1)));
	};
	genetic.crossover = function(mother, father) {

		// two-point crossover
		var len = mother.length;
		var ca = Math.floor(Math.random()*len);
		var cb = Math.floor(Math.random()*len);		
		if (ca > cb) {
			var tmp = cb;
			cb = ca;
			ca = tmp;
		}
			
		var son = father.substr(0,ca) + mother.substr(ca, cb-ca) + father.substr(cb);
		var daughter = mother.substr(0,ca) + father.substr(ca, cb-ca) + mother.substr(cb);
		
		return [son, daughter];
	};
	genetic.fitness = function(entity) {
		var fitness = 0;
		
		var i;
		for (i=0;i<entity.length;++i) {
			// increase fitness for each character that matches
			if (entity[i] == this.userData["solution"][i])
				fitness += 1;
			
			// award fractions of a point as we get warmer
			fitness += (127-Math.abs(entity.charCodeAt(i) - this.userData["solution"].charCodeAt(i)))/50;
		}

		return fitness;
	};
	genetic.generation = function(pop, generation, stats) {
		// stop running once we've reached the solution
		return pop[0].entity != this.userData["solution"];
	};
});


function solveTest(sel1, sel2, config) {
	it(sel1 + ", " + sel2, function (done) {
		genetic.select1 = eval(sel1);
		genetic.select2 = eval(sel2);
		genetic.notification = function(pop, generation, stats, isFinished) {
			if (isFinished) {
				assert.equal(pop[0].entity, this.userData["solution"]);
				done();
			}
		};
	
		var userData = {
			"solution": "thisisthesolution"
		};
	
		genetic.evolve(config, userData);
	});
}

describe("Genetic String Solver", function() {
	
	var config = {
		"iterations": 2000
		, "size": 20
		, "crossover": 0.4
		, "mutation": 0.3
	};
	
	
	var k;
	for (k in Genetic.Select1) {
		for (j in Genetic.Select2) {
			solveTest("Genetic.Select1."+k, "Genetic.Select2."+j, config);
		}
	};
	
});