GeneticAlgorithm.pde

GeneticAlgorithm.pde

class DNA{
  float[] x = new float[1000];
  float[] y = new float[1000];
  
  DNA(){
    for(int i = 0; i < x.length - 1; i++){
      x[i+1] += x[i] > 0 ? random(-1, 1) : random(0, 1);
      y[i+1] += y[i] > 0 ? random(-1, 1) : random(0, 1);
    }
  }
  
  float fitness(){
    return 100 * sqrt(pow(x[999], 2) + pow(y[999], 2)) / sqrt(500000);
  }
}

DNA[] population = new DNA[100];
int TimesOfGenerationalChange = 0;

void setup(){
  size(500, 500);
  for(int i = 0; i < population.length; i++){
    population[i] = new DNA();
  }
  
  for(int i = 0; i < population.length; i++){
    if(population[i].fitness() != 100){
      study();
      TimesOfGenerationalChange++;
    }else{
      break;
    }
  }
}

void study(){
  ArrayList<DNA> pool = new ArrayList<DNA>();
  
  for(int i = 0; i < population.length; i++){
    int n = int(population[i].fitness());
    
    for(int j = 0; j < n; j++){
      pool.add(population[i]);
    }
  }
  
  for(int j = 0; j < population.length; j++){
    int a = int(random(pool.size()));
    int b = int(random(pool.size()));
    population[j] = makechild(pool.get(a), pool.get(b));
  }
}

DNA makechild(DNA parent1, DNA parent2){
  DNA child = new DNA();
  
  println("a");
  for(int i = 0; i < child.x.length; i++){
    child.x[i] = i < int(random(child.x.length)) ? parent1.x[i] : parent2.x[i];
    child.y[i] = i < int(random(child.x.length)) ? parent1.y[i] : parent2.y[i];
  }
  println("b");
  return child;
}

void draw(){
  
}