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  my %fitness_of;
  for (my $i = 0; $i < $number_of_strings; $i++) {
   $population[$i] = random_chromosome( $length);
   $fitness_of{$population[$i]} = max_ones( $population[$i] );
  }

  my @best;
  my $generations=0;
  do {
    my @pool;
    if ( $generations % 2 == 1 ) {
      get_pool_roulette_wheel( \@population, \%fitness_of, $number_of_strings );
    } else {
     get_pool_binary_tournament( \@population, \%fitness_of, $number_of_strings );
    }
    my @new_pop = produce_offspring( \@pool, $number_of_strings/2 );
    for my $p ( @new_pop ) {
        if ( !$fitness_of{$p} ) {
	   $fitness_of{$p} = max_ones( $p );
	}
    }
    @best = rnkeytop { $fitness_of{$_} } $number_of_strings/2 => @population;
    @population = (@best, @new_pop);
    print "Best so far $best[0] with fitness $fitness_of{$best[0]}\n";
  } while ( ( $generations++ < $number_of_generations ) and ($fitness_of{$best[0]} != $length ));


=head1 DESCRIPTION

Assorted functions needed by an evolutionary algorithm, mainly for demos and simple clients.


=head1 INTERFACE 

=head2 random_chromosome( $length )

Creates a binary chromosome, with uniform distribution of 0s and 1s,
and returns it as a string.

=head2 max_ones( $string )

Classical function that returns the number of ones in a binary string.


=head2 max_ones_fast( $string )

Faster implementation of max_ones.

=head2 spin($wheel, $slots )

Mainly for internal use, $wheel has the normalized probability, and
  $slots the number of individuals to return.

=head2 single_generation( $population_arrayref, $fitness_of_hashref )

Applies all steps to arrive to a new generation, except
evaluation. Keeps the two best for the next generation.

=head2 get_pool_roulette_wheel( $population_arrayref, $fitness_of_hashref, $how_many_I_need )

Obtains a pool of new chromosomes using fitness_proportional selection


=head2 get_pool_binary_tournament( $population_arrayref, $fitness_of_hashref, $how_many_I_need )

Obtains a pool of new chromosomes using binary tournament, a greedier method.

=head2 produce_offspring( $population_hashref, $how_many_I_need )

Uses mutation first and then crossover to obtain a new population

=head2 mutate( $string )

Bitflips a a single point in the binary string

=head2 crossover( $one_string, $another_string )

Applies two-point crossover to both strings, returning them changed

=head1 DIAGNOSTICS

Will complain if some argument is missing.


Algorithm::Evolutionary::Simple requires no configuration files or environment variables.


=head1 DEPENDENCIES

L<Sort::Key::Top> for efficient sorting.

=head1 SEE ALSO

There are excellent evolutionary algorithm libraries out there; see
  for instance L<AI::Genetic::Pro>

=head1 BUGS AND LIMITATIONS

It's intended for simplicity, not flexibility. If you want a
  full-featured evolutionary algorithm library, check L<Algorithm::Evolutionary>

Please report any bugs or feature requests to
C<bug-algorithm-evolutionary-simple@rt.cpan.org>, or through the web interface at
L<http://rt.cpan.org>.


=head1 AUTHOR

JJ Merelo C<< <jj@merelo.net> >>


=head1 LICENCE AND COPYRIGHT

Copyright (c) 2011, JJ Merelo C<< <jj@merelo.net> >>. All rights reserved.

This module is free software; you can redistribute it and/or
modify it under the GPL v3 licence.


=head1 DISCLAIMER OF WARRANTY