Task: Evolutionary algorithm


C++: #include <string>
#include <cstdlib>
#include <iostream>
#include <cassert>
#include <algorithm>
#include <vector>
#include <ctime>

std::string allowed_chars = " ABCDEFGHIJKLMNOPQRSTUVWXYZ";






class selection
{
public:
  
  
  
  
  static int fitness(std::string candidate)
  {
    assert(target.length() == candidate.length());

    int fitness_so_far = 0;

    for (int i = 0; i < target.length(); ++i)
    {
      int target_pos = allowed_chars.find(target[i]);
      int candidate_pos = allowed_chars.find(candidate[i]);
      int diff = std::abs(target_pos - candidate_pos);
      fitness_so_far -= std::min(diff, int(allowed_chars.length()) - diff);
    }

    return fitness_so_far;
  }

  
  static int target_length() { return target.length(); }
private:
  static std::string target;
};

std::string selection::target = "METHINKS IT IS LIKE A WEASEL";


void move_char(char& c, int distance)
{
  while (distance < 0)
    distance += allowed_chars.length();
  int char_pos = allowed_chars.find(c);
  c = allowed_chars[(char_pos + distance) % allowed_chars.length()];
}



std::string mutate(std::string parent, double mutation_rate)
{
  for (int i = 0; i < parent.length(); ++i)
    if (std::rand()/(RAND_MAX + 1.0) < mutation_rate)
    {
      int distance = std::rand() % 3 + 1;
      if(std::rand()%2 == 0)
        move_char(parent[i], distance);
      else
        move_char(parent[i], -distance);
    }
  return parent;
}



bool less_fit(std::string const& s1, std::string const& s2)
{
  return selection::fitness(s1) < selection::fitness(s2);
}

int main()
{
  int const C = 100;

  std::srand(time(0));

  std::string parent;
  for (int i = 0; i < selection::target_length(); ++i)
  {
    parent += allowed_chars[std::rand() % allowed_chars.length()];
  }

  int const initial_fitness = selection::fitness(parent);

  for(int fitness = initial_fitness;
      fitness < 0;
      fitness = selection::fitness(parent))
  {
    std::cout << parent << ": " << fitness << "\n";
    double const mutation_rate = 0.02 + (0.9*fitness)/initial_fitness;
    std::vector<std::string> childs;
    childs.reserve(C+1);

    childs.push_back(parent);
    for (int i = 0; i < C; ++i)
      childs.push_back(mutate(parent, mutation_rate));

    parent = *std::max_element(childs.begin(), childs.end(), less_fit);
  }
  std::cout << "final string: " << parent << "\n";
}






Python: from string import letters
from random import choice, random
 
target  = list("METHINKS IT IS LIKE A WEASEL")
charset = letters + ' '
parent  = [choice(charset) for _ in range(len(target))]
minmutaterate  = .09
C = range(100)
 
perfectfitness = float(len(target))
    
def fitness(trial):
    'Sum of matching chars by position'
    return sum(t==h for t,h in zip(trial, target))
 
def mutaterate():
    'Less mutation the closer the fit of the parent'
    return 1-((perfectfitness - fitness(parent)) / perfectfitness * (1 - minmutaterate))
 
def mutate(parent, rate):
    return [(ch if random() <= rate else choice(charset)) for ch in parent]
 
def que():
    '(from the favourite saying of Manuel in Fawlty Towers)'
    print ("
           (iterations, fitness(parent)*100./perfectfitness, ''.join(parent)))

def mate(a, b):
    place = 0
    if choice(xrange(10)) < 7:
        place = choice(xrange(len(target)))
    else:
        return a, b
    
    return a, b, a[:place] + b[place:], b[:place] + a[place:]

iterations = 0
center = len(C)/2
while parent != target:
    rate = mutaterate()
    iterations += 1
    if iterations % 100 == 0: que()
    copies = [ mutate(parent, rate) for _ in C ]  + [parent]
    parent1 = max(copies[:center], key=fitness)
    parent2 = max(copies[center:], key=fitness)
    parent = max(mate(parent1, parent2), key=fitness)
que()