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";
}

Go: package main

import (
    "fmt"
    "math/rand"
    "time"
)

var target = []byte("METHINKS IT IS LIKE A WEASEL")
var set = []byte("ABCDEFGHIJKLMNOPQRSTUVWXYZ ")
var parent []byte

func init() {
    rand.Seed(time.Now().UnixNano())
    parent = make([]byte, len(target))
    for i := range parent {
        parent[i] = set[rand.Intn(len(set))]
    }
}


func fitness(a []byte) (h int) {
    
    for i, tc := range target {
        if a[i] != tc {
            h++
        }
    }
    return
}


func mutate(p, m []byte, r float64) {
    for i, ch := range p {
        if rand.Float64() < r {
            m[i] = set[rand.Intn(len(set))]
        } else {
            m[i] = ch
        }
    }
}

func main() {
    const c = 20 

    copies := make([][]byte, c)
    for i := range copies {
        copies[i] = make([]byte, len(parent))
    }

    fmt.Println(string(parent))
    for best := fitness(parent); best > 0; {
        for _, cp := range copies {
            mutate(parent, cp, .05)
        }
        for _, cp := range copies {
            fm := fitness(cp)
            if fm < best {
                best = fm
                copy(parent, cp)
                fmt.Println(string(parent))
            }
        }
    }
}