Hello_World_Genetic_Algorithm.js

function random(min, max) {
    min = Math.ceil(min);
    max = Math.floor(max);
  
    // The maximum is exclusive and the minimum is inclusive
    return Math.floor(Math.random() * (max - min)) + min;
  }
  
function generateLetter() {
    const code = random(97, 123); // ASCII char codes
    return String.fromCharCode(code);
}



class Member {
    constructor(goal){
        this.goal = goal;
        this.keys = [];

        for(let i = 0; i < goal.length; i+=1){
          this.keys[i] = generateLetter();
        }
    }

    fitness(){
        let match = 0;
        
        // finds the number of matches in comparison to the goal
        for(let i = 0; i < this.keys.length; i += 1){
            if(this.keys[i] == this.goal[i]){
                match += 1;
            }
        }
        
        return match / this.goal.length;
    }

    crossover(partner){
        const { length } = this.goal;
        const child = new Member(this.goal);
        const midpoint = random(0, length);

        // this randomly selects phenotypes to create a child
        // by randomly selecting a midpoint
        for(let i = 0; i < length; i+= 1){
            if(i > midpoint){
                child.keys[i] = this.keys[i];
            }
            else
            {
                child.keys[i] = partner.keys[i];
            }
        }

        return child;
    }

    mutate(mutationRate){
        for(let i = 0; i < this.keys.length; i += 1) {
            // if below prefedine mutation rate
            // generate a new random letter on this position
            if(Math.random() < mutationRate){
              this.keys[i] = generateLetter();
            }
        }
    }
}



class Population {
    constructor(size, goal, mutationRate){
        size = size || 1;
        this.members = [];
        this.mutationRate = mutationRate
        for(let i=0; i < size; i+=1){
            this.members.push(new Member(goal));
        }
    }

    _naturalSelection() {
        let matingPool = [];
    
        this.members.forEach( (m) => {
            // The more fit the mate, the more times it shows up in the mating pool
            // making more fit mates more likely to pass on their genetics
            // if fitness == 0, add just one member
            const f = Math.floor(m.fitness() * 100 || 1);

            // adds the mate once to the matingPool for each fitness
            for(let i = 0; i < f; i +=1){
              matingPool.push(m);
            }
        });

        return matingPool;
    }

    _reproduce(matingPool){
        for(let i = 0; i < this.members.length; i+= 1){
            // pick 2 random members/parents from the mating pool
            const parentA = matingPool[random(0, matingPool.length)];
            const parentB = matingPool[random(0, matingPool.length)];
            
            // perform crossover
            const child = parentA.crossover(parentB);

            // perform mutation
            child.mutate(this.mutationRate);

            this.members[i] = child;
        }
    }

    evolve(generations){
        for(let i = 0; i < generations; i += 1){
            const pool = this._naturalSelection();
            this._reproduce(pool);
        }
    }
}



function generate(populationSize, goal, mutationRate, generations) {
    // Create a population and evlove for N generations
    const population = new Population(populationSize, goal, mutationRate);
    population.evolve(generations);

    // Get the typed words from all members, and find if someone was able to type the target
    const membersKeys = population.members.map((m) => m.keys.join(''));
    const perfectCandidatesNum = membersKeys.filter((w) => w === goal);

    // Print the results
    console.log(membersKeys);
    console.log(`${perfectCandidatesNum ? perfectCandidatesNum.length : 0} member(s) typed "${goal}`);
  }

generate(250, 'helloworld', 0.025, 50);