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lib/Algorithm/Evolutionary/Run.pm  view on Meta::CPAN

sub reset_population {
  my $self = shift;
  #Initial population
  my @pop;

  #Creamos $popSize individuos
  my $bits = $self->{'length'}; 
  for ( 1..$self->{'pop_size'} ) {
      my $indi = Algorithm::Evolutionary::Individual::BitString->new( $bits );
      $indi->evaluate( $self->{'_fitness'} );
      push( @pop, $indi );
  }
  $self->{'_population'} = \@pop;
  $self->{'_fitness'}->reset_evaluations;
}

=head2 step()

Runs a single step of the algorithm, that is, a single generation 

=cut

sub step {
    my $self = shift;
    $self->{'_generation'}->apply( $self->{'_population'} );
    $self->{'_counter'}++;
}

=head2 run()

Applies the different operators in the order that they appear; returns the population
as a ref-to-array.

=cut

sub run {
  my $self = shift;
  $self->{'_counter'} = 0;
  do {
      $self->step();
      
  } while( ($self->{'_counter'} < $self->{'max_generations'}) 
	 && ($self->{'_population'}->[0]->Fitness() < $self->{'max_fitness'}));

}

=head2 random_member()

Returns a random guy from the population

=cut

sub random_member {
    my $self = shift;
    return $self->{'_population'}->[rand( @{$self->{'_population'}} )];
}

=head2 results()
 
Returns results in a hash that contains the best, total time so far
 and the number of evaluations. 

=cut

sub results {
  my $self = shift;
  my $population_size = scalar @{$self->{'_population'}};
  my $last_good_pos = $population_size*(1-$self->{'selection_rate'});
  my $results = { best => $self->{'_population'}->[0],
		  median => $self->{'_population'}->[ $population_size / 2],
		  last_good => $self->{'_population'}->[ $last_good_pos ],
		  time =>  tv_interval( $self->{'_start_time'} ),
		  evaluations => $self->{'_fitness'}->evaluations() };
  return $results;

}

=head2 evaluated_population()

Returns the portion of population that has been evaluated (all but the new ones)

=cut 

sub evaluated_population {
  my $self = shift;
  my $population_size = scalar @{$self->{'_population'}};
  my $last_good_pos = $population_size*(1-$self->{'selection_rate'}) - 1;
  return @{$self->{'_population'}}[0..$last_good_pos];
}


=head2 compute_average_distance( $individual )

Computes the average hamming distance to the population 

=cut

sub compute_average_distance {
  my $self = shift;
  my $other = shift || croak "No other\n";
  my $distance;
  for my $p ( @{$self->{'_population'}} ) {
    $distance += hamming( $p->{'_str'}, $other->{'_str'} );
  }
  $distance /= @{$self->{'_population'}};
}

=head2 compute_min_distance( $individual )

Computes the average hamming distance to the population 

=cut

sub compute_min_distance {
  my $self = shift;
  my $other = shift || croak "No other\n";
  my $min_distance = length( $self->{'_population'}->[0]->{'_str'} );
  for my $p ( @{$self->{'_population'}} ) {
    my $this_distance = hamming( $p->{'_str'}, $other->{'_str'} );
    $min_distance = ( $this_distance < $min_distance )?$this_distance:$min_distance;
  }
  return $min_distance;

}

=head1 Copyright
  
  This file is released under the GPL. See the LICENSE file included in this distribution,
  or go to http://www.fsf.org/licenses/gpl.txt