A Simple Genetic Algorithms in Javascript

SimpleDemoGA.js

/*
Based on the Vijini Mallawaarachchi's article in Towards Data Science
https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3
*/

"strict mode"

function Individual(numOfGenes) {
    const _genes = Array(numOfGenes).fill(0).map(i => Math.floor(Math.random() * 1000 % 2));
    let _fitness = 0;
    return {
        fitness: () => _fitness,
        genes: (index, value) => {
            if(!index) {
                return _genes;
            }
            if (!value) {
                return _genes[index];
            }
            return (_genes[index] = value);
        },
        computeFitness: () => {
            _fitness = _genes.reduce((a, c) => a += c);
        },
        mutate: () => {
            let mutationPoint = Math.floor(Math.random() * 1000 % numOfGenes);
            _genes[mutationPoint] = _genes[mutationPoint] ^ 1;
        }
    }
}

function Population(numOfIndividuals, numOfGenes) {
    const _individuals = Array(numOfIndividuals).fill(0).map(() => new Individual(numOfGenes));
    let _fittest;
    let _secondFittest;
    return {
        bestFitness: () => _fittest.fitness(),
        bestGenes: () => _fittest.genes().join(""),
        evaluate: () => {
            _individuals.forEach(i => i.computeFitness());
            _individuals.sort((i1, i2) => i2.fitness() - i1.fitness());
        },
        selection: () => {
            _fittest = _individuals[0];
            _secondFittest = _individuals[1];
        },
        crossover: () => {
            let crossOverPoint = Math.floor(Math.random() * 1000 % numOfGenes);
            for (let i = 0; i < crossOverPoint; i++) {
                let g1 = _fittest.genes(i);
                let g2 = _secondFittest.genes(i);
                _fittest.genes(i, g2);
                _secondFittest.genes(i, g1);
            }
        },
        mutation: () => {
            if (Math.floor(Math.random() * 1000 % 7) >= 5) {
                return;
            }
            _fittest.mutate();
            _secondFittest.mutate();
        },
        addFittestOffspring: () => {
            _fittest.computeFitness();
            _secondFittest.computeFitness();
            let fittestOffspring = (_fittest.fitness > _secondFittest.fitness) ? _fittest : _secondFittest;
            _individuals[numOfIndividuals - 1] = {...fittestOffspring};
        }
    }
}

function SimpleDemoGA() {
    return {
        execute: (numOfIndividuals = 10, numOfGenes = 5, maxGenerations = 100) => {
            const population = new Population(numOfIndividuals, numOfGenes);
            let generationCount = 0;
            do {
                population.evaluate();
                population.selection();
                population.crossover();
                population.mutation();
                population.addFittestOffspring();
                generationCount++;
                console.log(`Generation ${generationCount} Fittest: ${population.bestFitness()}`);
            } while (population.bestFitness() < numOfGenes && generationCount < maxGenerations);
            console.log("");
            console.log(`Solution found in generation ${generationCount}`);
            console.log(`Fitness: ${population.bestFitness()}`);
            console.log(`Genes: ${population.bestGenes()}`);
        }
    }
}

new SimpleDemoGA().execute(numOfIndividuals=10, numOfGenes=5, maxGenerations=100);