Task: Evolutionary algorithm







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()





Ada: with Ada.Text_IO;
with Ada.Numerics.Discrete_Random;
with Ada.Numerics.Float_Random;
with Ada.Strings.Fixed;
with Ada.Strings.Maps;

procedure Evolution is

   
   
   subtype DNA_Char is Character range '@' .. 'Z';

   
   subtype DNA_String is String (1 .. 28);

   
   Target : constant DNA_String := "METHINKS@IT@IS@LIKE@A@WEASEL";

   
   
   
   
   function Fitness (DNA : DNA_String) return Natural is
      Result : Natural := 0;
   begin
      for Position in DNA'Range loop
         if DNA (Position) = Target (Position) then
            Result := Result + 1;
         end if;
      end loop;
      return Result;
   end Fitness;

   
   procedure Output_DNA (DNA : DNA_String; Prefix : String := "") is
      use Ada.Strings.Maps;
      Output_Map : Character_Mapping;
   begin
      Output_Map := To_Mapping
        (From => To_Sequence (To_Set (('@'))),
         To   => To_Sequence (To_Set ((' '))));
      Ada.Text_IO.Put (Prefix);
      Ada.Text_IO.Put (Ada.Strings.Fixed.Translate (DNA, Output_Map));
      Ada.Text_IO.Put_Line (", fitness: " & Integer'Image (Fitness (DNA)));
   end Output_DNA;

   
   package Random_Char is new Ada.Numerics.Discrete_Random (DNA_Char);
   DNA_Generator : Random_Char.Generator;

   
   Float_Generator : Ada.Numerics.Float_Random.Generator;

   
   function Mutate (Parent        : DNA_String;
                    Mutation_Rate : Float)
                    return          DNA_String
   is
      Result : DNA_String := Parent;
   begin
      for Position in Result'Range loop
         if Ada.Numerics.Float_Random.Random (Float_Generator) <= Mutation_Rate
         then
            Result (Position) := Random_Char.Random (DNA_Generator);
         end if;
      end loop;
      return Result;
   end Mutate;

   
   
   procedure Evolve (Child_Count   : Positive := 100;
                     Mutation_Rate : Float    := 0.2)
   is
      type Child_Array is array (1 .. Child_Count) of DNA_String;

      
      function Fittest (Candidates : Child_Array) return DNA_String is
         The_Fittest : DNA_String := Candidates (1);
      begin
         for Candidate in Candidates'Range loop
            if Fitness (Candidates (Candidate)) > Fitness (The_Fittest)
            then
               The_Fittest := Candidates (Candidate);
            end if;
         end loop;
         return The_Fittest;
      end Fittest;

      Parent, Next_Parent : DNA_String;
      Children            : Child_Array;
      Loop_Counter        : Positive := 1;
   begin
      
      for Position in Parent'Range loop
         Parent (Position) := Random_Char.Random (DNA_Generator);
      end loop;
      Output_DNA (Parent, "First: ");
      while Parent /= Target loop
         
         for Child in Children'Range loop
            Children (Child) := Mutate (Parent, Mutation_Rate);
         end loop;
         Next_Parent := Fittest (Children);
         
         if Fitness (Next_Parent) > Fitness (Parent) then
            Parent := Next_Parent;
         end if;
         
         if Loop_Counter mod 20 = 0 then
            Output_DNA (Parent, Integer'Image (Loop_Counter) & ": ");
         end if;
         Loop_Counter := Loop_Counter + 1;
      end loop;
      Output_DNA (Parent, "Final (" & Integer'Image (Loop_Counter) & "): ");
   end Evolve;

begin
   
   Random_Char.Reset (DNA_Generator);
   Ada.Numerics.Float_Random.Reset (Float_Generator);
   
   Evolve;
end Evolution;