constructor(populationSize, chromosomeLength, mutationRate, crossoverRate, elitismCount) {

    this.populationSize = populationSize;

    this.chromosomeLength = chromosomeLength;

    this.mutationRate = mutationRate;

    this.crossoverRate = crossoverRate;

    this.elitismCount = elitismCount;

    this.population = [];

  }

​

  initialize() {

    for (let i = 0; i < this.populationSize; i++) {

      const chromosome = [];

      for (let j = 0; j < this.chromosomeLength; j++) {

        chromosome.push(Math.round(Math.random()));

      }

      this.population.push(chromosome);

    }

  }

​

  calculateFitness(fitnessFunction) {

    for (let i = 0; i < this.populationSize; i++) {

      const chromosome = this.population[i];

      const fitness = fitnessFunction(chromosome);

      this.population[i] = { chromosome, fitness };

    }

  }

​

  selection() {

    const sortedPopulation = this.population.sort((a, b) => b.fitness - a.fitness);

    return sortedPopulation.slice(0, this.elitismCount).map((individual) => individual.chromosome);

  }

​

  crossover(parent1, parent2) {

    const crossoverPoint = Math.floor(Math.random() * this.chromosomeLength);

    const offspring1 = parent1.slice(0, crossoverPoint).concat(parent2.slice(crossoverPoint));

    const offspring2 = parent2.slice(0, crossoverPoint).concat(parent1.slice(crossoverPoint));

    return [offspring1, offspring2];

  }

​

  mutation(chromosome) {

    for (let i = 0; i < this.chromosomeLength; i++) {

      if (Math.random() < this.mutationRate) {

        chromosome[i] = chromosome[i] === 0 ? 1 : 0;

      }

    }

    return chromosome;

  }

​

  evolve(fitnessFunction) {

    this.calculateFitness(fitnessFunction);

    const newPopulation = this.selection();

​

    while (newPopulation.length < this.populationSize) {

      const parent1 = this.population[Math.floor(Math.random() * this.elitismCount)].chromosome;

      const parent2 = this.population[Math.floor(Math.random() * this.elitismCount)].chromosome;

      let offspring = this.crossover(parent1, parent2);

      offspring = offspring.map((child) => this.mutation(child));

      newPopulation.push(...offspring);

    }

​

    this.population = newPopulation;

  }

}

​

// Define the fitness function

const fitnessFunction = (chromosome) => {

  let fitness = 0;

  for (let i = 0; i < chromosome.length; i++) {

    fitness += chromosome[i];

  }

  return fitness;

};

​

// Create an instance of the GeneticAlgorithm class

const ga = new GeneticAlgorithm(100, 10, 0.01, 0.8, 5);

​

// Initialize the population

ga.initialize();

​

// Evolve the population for 100 generations

for (let i = 0; i < 100; i++) {

  ga.evolve(fitnessFunction);

}

​

// Get the best chromosome

const bestChromosome = ga.population[0].chromosome;

​

console.log('Best chromosome:', bestChromosome);

​