Task: Evolutionary algorithm



C#: using System;
using System.Collections.Generic;
using System.Linq;

static class Program {
    static Random Rng = new Random((int)DateTime.Now.Ticks);

    static char NextCharacter(this Random self) {
        const string AllowedChars = " ABCDEFGHIJKLMNOPQRSTUVWXYZ";
        return AllowedChars[self.Next() % AllowedChars.Length];
    }

    static string NextString(this Random self, int length) {
        return String.Join("", Enumerable.Repeat(' ', length)
            .Select(c => Rng.NextCharacter()));
    }

    static int Fitness(string target, string current) {
        return target.Zip(current, (a, b) => a == b ? 1 : 0).Sum();
    }

    static string Mutate(string current, double rate) {
        return String.Join("", from c in current
               select Rng.NextDouble() <= rate ? Rng.NextCharacter() : c);
    }

    static void Main(string[] args) {
        const string target = "METHINKS IT IS LIKE A WEASEL";
        const int C = 100;
        const double P = 0.05;

        
        string parent = Rng.NextString(target.Length);

        Console.WriteLine("START:       {0,20} fitness: {1}", 
            parent, Fitness(target, parent));
        int i = 0;

        while (parent != target) {
            
            var candidates = Enumerable.Range(0, C + 1)
                .Select(n => n > 0 ? Mutate(parent, P) : parent);

            
            parent = candidates.OrderByDescending(c => Fitness(target, c)).First();

            ++i;
            Console.WriteLine("     #{0,6} {1,20} fitness: {2}", 
                i, parent, Fitness(target, parent));
        }

        Console.WriteLine("END: #{0,6} {1,20}", i, parent);
    }
}







COBOL: identification division.
program-id. evolutionary-program.
data division.
working-storage section.
01  evolving-strings.
    05 target                pic a(28)
        value 'METHINKS IT IS LIKE A WEASEL'.
    05 parent                pic a(28).
    05 offspring-table.
        10 offspring         pic a(28)
            occurs 50 times.
01  fitness-calculations.
    05 fitness               pic 99.
    05 highest-fitness       pic 99.
    05 fittest               pic 99.
01  parameters.
    05 character-set         pic a(27)
        value 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '.
    05 size-of-generation    pic 99
        value 50.
    05 mutation-rate         pic 99
        value 5.
01  counters-and-working-variables.
    05 character-position    pic 99.
    05 randomization.
        10 random-seed       pic 9(8).
        10 random-number     pic 99.
        10 random-letter     pic 99.
    05 generation            pic 999.
    05 child                 pic 99.
    05 temporary-string      pic a(28).
procedure division.
control-paragraph.
    accept random-seed from time.
    move function random(random-seed) to random-number.
    perform random-letter-paragraph,
    varying character-position from 1 by 1
    until character-position is greater than 28.
    move temporary-string to parent.
    move zero to generation.
    perform output-paragraph.
    perform evolution-paragraph,
    varying generation from 1 by 1
    until parent is equal to target.
    stop run.
evolution-paragraph.
    perform mutation-paragraph varying child from 1 by 1
    until child is greater than size-of-generation.
    move zero to highest-fitness.
    move 1 to fittest.
    perform check-fitness-paragraph varying child from 1 by 1
    until child is greater than size-of-generation.
    move offspring(fittest) to parent.
    perform output-paragraph.
output-paragraph.
    display generation ': ' parent.
random-letter-paragraph.
    move function random to random-number.
    divide random-number by 3.80769 giving random-letter.
    add 1 to random-letter.
    move character-set(random-letter:1)
    to temporary-string(character-position:1).
mutation-paragraph.
    move parent to temporary-string.
    perform character-mutation-paragraph,
    varying character-position from 1 by 1
    until character-position is greater than 28.
    move temporary-string to offspring(child).
character-mutation-paragraph.
    move function random to random-number.
    if random-number is less than mutation-rate
    then perform random-letter-paragraph.
check-fitness-paragraph.
    move offspring(child) to temporary-string.
    perform fitness-paragraph.
fitness-paragraph.
    move zero to fitness.
    perform character-fitness-paragraph,
    varying character-position from 1 by 1
    until character-position is greater than 28.
    if fitness is greater than highest-fitness
    then perform fittest-paragraph.
character-fitness-paragraph.
    if temporary-string(character-position:1) is equal to
    target(character-position:1) then add 1 to fitness.
fittest-paragraph.
    move fitness to highest-fitness.
    move child to fittest.