BOL: Genetic Algorithms demonstration with word DNA in Perl

BioScripts
Archana Malhotra
Genetic Algorithms demonstration with word DNA in Perl
Genetic Algorithms demonstration with word DNA in Perl

#!/usr/bin/perl -w
# GA demonstration with word DNA (512 bits)
use strict;
use Data::Dumper;
# individuals in the population
my $popsize = 1024;         # a good starting point
my $dna_length = 512;           # 4 "letters" in the DNA
my $dna_byte_length = $dna_length / 8;  # the DNA byte length
my $mut_rate = 0.01;            # the mutation rate
my $min_fitness = 0.1;          # the minimum fitness for survival
my $generation_count = 100000;          # run for this many generations
my $generation = 0;         # generation counter
my $pop_ref = [];                       # a reference to a population array



sub evaluate_fitness {
 my $population = shift @_;
 my $fitness_function = shift @_;
 foreach my $individual (@$population) {
  # set the fitness to the result of invoking the fitness function
  # on the individual's DNA
  $individual->{fitness} = $fitness_function->($individual->{dna});
 }
}
sub survive{
 my $population = shift @_;
 my $min_fitness = shift @_;
 my $survived = 0;
 foreach my $individual (@$population)
 {
  # set the fitness to 0 for unfit individuals (so they won't procreate)
  $individual->{survived} = $individual->{fitness} >= $min_fitness;
  if ($individual->{survived})
  {
   $survived++;
  }
  else
  {
   $individual->{fitness} = 0
  }
 }
 if (0 == $survived)
 {
  die "No individuals survived, dying peacefully";
 }
}
sub select_parents {
 my $population = shift @_;
 my $pop_size = scalar @$population;    # population size
 # create the weights array: select only survivors from the population,
 # then use map to have only the fitness come through
 my @weights = map { $_->{fitness} } grep { $_->{survived} } 
   @$population;
 # if we have less than 2 survivors, we're in trouble
 die "Population size $pop_size is too small" if $pop_size < 2;
 # we need to fill $pop_size parenting slots, to preserve the population size
 foreach my $slot (1..$pop_size)
 {
  my $index = sample(\@weights); 
  # we pass a reference to the weights array here
  # do sanity checking on $index
  die "Undefined index returned by sample(), probably 
      all individuals have died"
   unless defined $index;
  die "Invalid index $index returned by sample()"
   unless $index >= 0 && $index < $pop_size;
  # increase the parenting slots for this population member
  $population->[$index]->{parent}++;
 }
}
sub recombine {
 my $population = shift @_;
 my $pop_size = scalar @$population;    # population size
 my @parent_population;
 my @new_population;
 my $total_parent_slots = 1;
 while ($total_parent_slots)
 {
  # find out how many parent slots are left
  $total_parent_slots = 0;
  $total_parent_slots += $_->{parent} foreach @$population;
  last unless $total_parent_slots;
  # if we are here, we're sure we have at least one individual with 
  # parent > 0
  my $individual = undef;       # start with an undefined individual
  do
  {
   # select a random individual
   $individual = $population->[int(rand($pop_size))];
   # individual is acceptable only if he can be a parent
   undef($individual) unless $individual->{parent};
  } while (not defined $individual);
  push @parent_population, $individual; 
  # insert the individual in the parent population
  $individual->{parent}--;       
  # decrease the parenting slots of the individual by 1
 }
 foreach my $parent (@parent_population)
 {
  # select a random individual from the parent population (parent #2)
  my $parent2 = @parent_population[int(rand($pop_size))];
  my $child = { survived => 1, parent => 0, 
    fitness => 0, dna => 0 };
  # this is breeding!
  my $dna1 = $parent->{dna};
  my $dna2 = $parent2->{dna};  
  # note we do operations on BYTES, not BITS.  This is because bytes
  # are the unit of information (and preserving them is the faster
  # breeding method)
  foreach my $byte (1 .. $dna_byte_length)
  {
   # get one random byte from the parents and add it to the child
   vec($child->{dna}, $byte-1, 8) = vec(((rand() < 0.5) ? $dna1 :
   $dna2), $byte-1, 8);
  }
  push @new_population, $child;     
  # the child is now a part of the new generation
 }
 return \@new_population;
}
sub mutate {
 my $population = shift @_;
 my $mut_rate   = shift @_;
 foreach my $individual (@$population)
 {
  # only mutate individuals if rand() returns more than mut_rate
  next if rand > $mut_rate;
  # mutate the DNA by and-ing and then or-ing it with two random
  # integers between 0 and 2^$dna_length
  my $old_dna = $individual->{dna};
  my $new_dna = 0;
  foreach my $byte (1 .. $dna_byte_length)
  {
   vec($new_dna, $byte-1, 8) &= int(rand(256));
   vec($new_dna, $byte-1, 8) |= int(rand(256));
  }
  $individual->{dna} = $new_dna;
#  print "Mutated $old_dna to ", $individual->{dna}, "\n";
 }
}
# this is a closure block!
{                               
 # private static variable @dictionary in closure for fitness() only
 my @dictionary;
 my %freqs;
 # calculate the fitness of the DNA
 sub fitness {
  my $dna = shift @_;  
  my $words = dna_to_words($dna);
  my $fitness = 0;          # start with 0 fitness
  my $max_entry_length = 20;        # longest word we accept
  # you can use any word list at the end of the program
  # do the @dictionary initialization just once
  unless (@dictionary)
  {
   @dictionary = '';
   foreach (@dictionary)
   {
    chomp;
   }   
   # eliminate words over $max_entry_length letters, and uppercase them
   @dictionary = grep { length($_) > 1  && length($_) 
      < $max_entry_length }
    map { uc } @dictionary;
   # build the letter frequencies hash (remember, all letters are uppercase)
   $freqs{$_}++ foreach split '', join '', @dictionary;
  }
  # there is no easy way to avoid this exhaustive check of the dictionary
  # without complicating this example too much
  foreach my $entry (@dictionary, 'A'..'Z')
  {
   # do nothing if the entry is not matched in the DNA, or vice versa
   next unless $words =~ /$entry/;
   # we have a match!  (it may be a substring, that's OK)
   # increment the fitness depending on how long the match was;
   $fitness += 2**length($entry);
   $fitness+= $freqs{$entry} if exists $freqs{$entry};
  }
  return $fitness;
 } # end of fitness()
}
# Function to sample from an array of weighted elements
# originally written by Abigail <abigail@foad.org>
# Documentation for the algorithm is at
# http://theoryx5.uwinnipeg.ca/CPAN/data/Sample/Sample.html
# (the CPAN Sample module)
sub sample {
 # get the reference to the weights array
 my $weights = shift @_ or return undef;
 # internal counting variables
 my ($count, $sample, @count);
 for (my $i  = 0; $i < scalar @$weights; $i ++)
 {
  $count += $weights->[$i];
  $sample = $i if rand $count [$i];
 }
 # return an index into the weights array
 return $sample;
}
# ASCII-centric byte to letter conversion
sub byte_to_letter {
 my $dna = shift @_;
 my $byte = shift @_;
# print "Got byte $byte\n";
 my $letter = vec $dna, $byte, 8;
 # is the byte in the letter ranges? if so, return it.
 return chr($letter) if ($letter >= 65 && $letter <= 90);
 # if not, return a space.  the use of ord() every time could be cached.
 return ' '; 
}
# print the DNA out to a scalar
sub dna_to_words {
 my $dna = shift @_;
 my @words;
 foreach my $byte (1.. $dna_byte_length)
 {
  # print the letter equivalent of the current byte
  push @words, byte_to_letter($dna, $byte-1);
 }
 # return the printable words
 return join '', @words;
}




init_population($pop_ref, $popsize);
do
{
 evaluate_fitness($pop_ref, \&fitness);
 # print out a generation summary line
 my @sorted_population = sort { $a->{fitness} cmp $b->{fitness} } @$pop_ref;
 printf "generation %d: size %dnleast fit DNA [%s]/%d\n
  most fit DNA  [%s]/%d\n",
  $generation,
   scalar @sorted_population,
    dna_to_words($sorted_population[0]->{dna}),
     $sorted_population[0]->{fitness},
      dna_to_words($sorted_population[-1]->{dna}),
       $sorted_population[-1]->{fitness};
 survive($pop_ref, $min_fitness);       # select survivors from the population
 select_parents($pop_ref);
 $pop_ref = recombine($pop_ref);       
  # recombine() returns a whole new population array reference
 # from this point on, we are working with a new generation in $pop_ref
 mutate($pop_ref, $mut_rate);           # apply mutation to the individuals
} while ($generation++ < $generation_count); 
# run until we are out of generations
sub init_population {
 my $population = shift @_;
 my $pop_size = shift @_;
 # for each individual
 foreach my $id (1 .. $pop_size) {
  # insert an anonymous hash reference in the population array 
  # with the individual's data
  # the DNA is a random number
  my $random_dna = 0;
  foreach my $byte (1 .. $dna_byte_length)
  {
   vec($random_dna, $byte-1, 8) = int(rand(256));
# printf "Byte $byte; Random DNA is now [%64s]\n", dna_to_words($random_dna);
  }
  push @$population, { dna => $random_dna, survived => 1, 
  parent => 0, fitness => 0 };
 }
}

__DATA__
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Genetic Algorithms demonstration with word DNA in Perl
