Algorithm-Evolutionary
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* MANIFEST: 0.70 fixes a bug in Easy.pm and those derived from it:
last population element was not eliminated. An animated GIF output
has been added, and a bug in Makefile.PL that caused problems with
Solaris has also been fixed (Thanks to Alex Muntada).
2009-07-26 <jmerelo@localhost.localdomain>
* lib/Algorithm/Evolutionary.pm (import): 0.69_1 fixed some
problems with missing prerrequisites in the test programs, and
some copy/paste stuff left from creating new modules. 0.70 starts
with the intention of finally having a GUI.
2009-07-24 <jmerelo@localhost.localdomain>
* lib/Algorithm/Evolutionary.pm: 0.68 was shortlived, but 0.69
more or less the same. 0.69 included missing file and fixed some
docs errors; 0.69_1 starts with the same objective, let's see how
it ends.
2009-07-24 Juan Julian Merelo Guervos <jmerelo@geneura.ugr.es>
* TODO: 0.68 ends with several new test functions added (Trap,
ECC), and finally a multiobjective evolutionary algorithm, not
very good, but in working order.
2009-03-29 <jmerelo@localhost.localdomain>
* lib/Algorithm/Evolutionary.pm: 0.68 starts with the usual
cosmetic changes to the documentation.
* Added "rough consensus" utility as well as a "convergence
terminator", which checks whether a part of the population has
converged. Upgraded to 0.67. Already!
2009-02-21 jmerelo <jmerelo@localhost.localdomain>
* lib/Algorithm/Evolutionary.pm (import): Changing to 0.64_2 to
eventually fix the 0500-generation-skel.t, which was not written
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lib/Algorithm/Evolutionary/Individual/Bit_Vector.pm view on Meta::CPAN
sub TIEARRAY {
my $class = shift;
my $self = { _bit_vector => Bit::Vector->new_Bin(scalar( @_), join("",@_)) };
bless $self, $class;
return $self;
}
=head2 Atom
Sets or gets the value of the n-th character in the string. Counting
starts at 0, as usual in Perl arrays.
=cut
sub Atom: lvalue {
my $self = shift;
my $index = shift;
my $last_index = $self->{'_bit_vector'}->Size()-1;
if ( @_ ) {
$self->{'_bit_vector'}->Bit_Copy($last_index-$index, shift );
} else {
lib/Algorithm/Evolutionary/Individual/String.pm view on Meta::CPAN
my $str = $self->{'_str'} . " -> ";
if ( defined $self->{'_fitness'} ) {
$str .=$self->{'_fitness'};
}
return $str;
}
=head2 Atom
Sets or gets the value of the n-th character in the string. Counting
starts at 0, as usual in Perl arrays.
=cut
sub Atom {
my $self = shift;
my $index = shift;
if ( @_ ) {
substr( $self->{_str}, $index, 1 ) = substr(shift,0,1);
} else {
substr( $self->{_str}, $index, 1 );
lib/Algorithm/Evolutionary/Individual/Vector.pm view on Meta::CPAN
=head1 SYNOPSIS
use Algorithm::Evolutionary::Individual::Vector;
my $indi = new Algorithm::Evolutionary::Individual::Vector 10 ; # Build random vector individual with length 10
# Each element in the range 0 .. 1
my $indi2 = new Algorithm::Evolutionary::Individual::Vector 20, -5, 5; #Same, with range between -5 and 5
#Creating a vector step by step. In Perl, there's always more than one way of doing it
my $indi3 = new Algorithm::Evolutionary::Individual::Vector;
$indi3->set( {length => 20,
rangestart => -5,
rangeend => 5 } ); #Sets values, but does not build the array
$indi3->randomize(); #Creates an array using above parameters
print $indi3->Atom( 7 ); #Returns the value of the 7th character
$indi3->Atom( 3 ) = '2.35'; #Sets the value
$indi3->addAtom( 7.5 ); #Adds a new component to the array at the end
my $indi4 = Algorithm::Evolutionary::Individual::Vector->fromString( '3.5,4.5, 0.1, 3.2');
lib/Algorithm/Evolutionary/Individual/Vector.pm view on Meta::CPAN
use Carp;
use Exporter;
our ($VERSION) = ( '$Revision: 3.2 $ ' =~ / (\d+\.\d+)/ );
use base 'Algorithm::Evolutionary::Individual::Base';
=head1 METHODS
=head2 new( [$length = 10] [, $start_of_range = 0] [, $end_of_range = 1] )
Creates a new random array individual, with fixed initial length, and uniform distribution
of values within a range
=cut
sub new {
my $class = shift;
my $self;
$self->{_length} = shift || 10;
$self->{_array} = ();
$self->{_rangestart} = shift || 0;
$self->{_rangeend } = shift || 1;
$self->{_fitness} = undef;
bless $self, $class;
$self->randomize();
return $self;
}
=head2 size()
lib/Algorithm/Evolutionary/Individual/Vector.pm view on Meta::CPAN
my $self = { _array => \@_,
_length => scalar( @_ ),
_fitness => undef };
bless $self, $class;
return $self;
}
=head2 set( $ref_to_hash )
Sets values of an individual; takes a hash as input. The array is
initialized to a null array, and the start and end range are
initialized by default to 0 and 1
=cut
sub set {
my $self = shift;
my $hash = shift || croak "No params here";
for ( keys %{$hash} ) {
$self->{"_$_"} = $hash->{$_};
}
$self->{_array} = shift || ();
$self->{_rangestart} = $self->{_rangestart} || 0;
$self->{_rangeend} = $self->{_rangeend} || 1;
$self->{_fitness} = undef;
}
=head2 randomize()
Assigns random values to the elements
=cut
sub randomize {
my $self = shift;
my $range = $self->{_rangeend} - $self->{_rangestart};
for ( my $i = 0; $i < $self->{_length}; $i++ ) {
push @{$self->{_array}}, rand( $range ) + $self->{_rangestart};
}
}
=head2 Atom
Gets or sets the value of an atom
=cut
sub Atom{
lib/Algorithm/Evolutionary/Op/GaussianMutation.pm view on Meta::CPAN
my $self = shift;
my $arg = shift || croak "No victim here!";
croak "Incorrect type".(ref $arg) if !$arg->{_array};
my $victim = clone($arg);
my @deltas = random_normal( @{$victim->{_array}} + 1, #+1 is needed, returns empty if not
$self->{_avg}, $self->{_stddev} );
for ( @{$victim->{_array}} ) {
my $adjust = pop @deltas;
$_ += $adjust;
# makes sure that the new value stays within its confines
if($_ < $victim->{_rangestart}) {
$_ = $victim->{_rangestart};
} elsif($_ > $victim->{_rangeend}) {
$_ = $victim->{_rangeend};
}
}
$victim->{_fitness} = undef;
return $victim;
}
=head1 THANKS
lib/Algorithm/Evolutionary/Run.pm view on Meta::CPAN
#----------------------------------------------------------#
bless $self, $class;
$self->reset_population;
for ( @{$self->{'_population'}} ) {
if ( !defined $_->Fitness() ) {
$_->evaluate( $fitness_object );
}
}
$self->{'_generation'} = $generation;
$self->{'_start_time'} = $inicioTiempo;
return $self;
}
=head2 population_size( $new_size )
Resets the population size to the C<$new_size>. It does not do
anything to the actual population, just resests the number. You should
do a C<reset_population> afterwards.
=cut
lib/Algorithm/Evolutionary/Run.pm view on Meta::CPAN
=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
scripts/canonical-genetic-algorithm.pl view on Meta::CPAN
=head1 SYNOPSIS
prompt% ./canonical-genetic-algorithm.pl <bits> <block size> <population> <number of generations> <selection rate>
=head1 DESCRIPTION
A canonical GA uses mutation, crossover, binary representation, and
roulette wheel selection. Here mainly for reference, and so that
you can peruse to start your own programs.
In this case, we are optimizing the Royal Road function,
L<http://web.cecs.pdx.edu/~mm/handbook-of-ec-rr.pdf>. By default,
these values are used:
=over
=item *
I<number of bits>: 64 (this is the chromosome length)
scripts/rectangle-coverage.pl view on Meta::CPAN
=head1 DESCRIPTION
A demo that combines the L<Algorithm::Evolutionary::Op::Easy> module
with L<Tk> to create a visual demo of the evolutionary
algorithm. It generates randomly a number of rectangles, and shows
how the population evolves to find the solution. The best point is
shown in darkening yellow color, the rest of the population in
green.
Use "Start" to start the algorithm after setting the variables, and
then Finish to stop the EA, Exit to close the window.
Default values are as follows
=over
=item *
I<number of rectangles>: 300
scripts/rectangle-coverage.pl view on Meta::CPAN
-bd => 2)->pack(-side => 'top',
-fill => 'x');
for my $v ( qw( num_rects arena_side bits pop_size number_of_generations selection_rate ) ){
create_and_pack( $f, $v );
}
my $canvas = $mw->Canvas( -cursor=>"crosshair", -background=>"white",
-width=>$width, -height=>$height )->pack;
$mw->Button( -text => 'Start',
-command => \&start,
)->pack( -side => 'left',
-expand => 1);
$mw->Button( -text => 'End',
-command => \&finished,
)->pack( -side => 'left',
-expand => 1 );
$mw->Button( -text => 'Exit',
-command => sub { exit(0);},
)->pack( -side => 'left',
-expand => 1 );
scripts/rectangle-coverage.pl view on Meta::CPAN
sub create_and_pack {
my $frame = shift;
my $var = shift;
my $f = $frame->Frame();
my $label = $f->Label(-text => $var )->pack(-side => 'left');
my $entry = $f->Entry( -textvariable => eval '\$'.$var )->pack(-side => 'right' );
$f->pack();
}
sub start {
#Generate random rectangles
for my $i (0 .. $num_rects) {
my $x_0 = rand( $arena_side );
my $y_0 = rand( $arena_side);
my $side_x = rand( $arena_side - $x_0 );
my $side_y = rand($arena_side-$y_0);
$alg->add_rectangle("rectangle_$i", $x_0, $y_0,
$x_0+$side_x, $x_0+$side_y );
my $val = 255*$i/$num_rects;
t/0408-gaussian-mutation.t view on Meta::CPAN
my $sm = new Algorithm::Evolutionary::Op::GaussianMutation $center, $sd;
isa_ok( $sm, 'Algorithm::Evolutionary::Op::GaussianMutation' );
my $result;
for ( 1..100 ) {
$result = $sm->apply( $indi );
for ( my $i = 0; $i < $indi->size(); $i ++ ) {
isnt( $result->Atom($i), $indi->Atom($i),
$result->Atom($i)." differs from ". $indi->Atom($i));
isnt( $result->Atom($i) > $indi->{'_rangeend'}, 1, 'Within upper bounds');
isnt( $result->Atom($i) < $indi->{'_rangestart'}, 1, 'Within lower bounds');
}
}
done_testing();
t/general.t view on Meta::CPAN
print "BitString...\n";
my $bs = Algorithm::Evolutionary::Individual::BitString->new(100);
is( ref $bs, "Algorithm::Evolutionary::Individual::BitString", , "Good ref" );
is( ref Algorithm::Evolutionary::Individual::Base::create( 'BitString', { length => 10 }), "Algorithm::Evolutionary::Individual::BitString", "Good ref" );
#Vector - 5..7
print "Vector...\n";
is( ref Algorithm::Evolutionary::Individual::Vector->new(10), "Algorithm::Evolutionary::Individual::Vector", "Good ref" );
is( ref Algorithm::Evolutionary::Individual::Base::create( 'Vector',
{ length => 20,
rangestart => -5,
rangeend => 5 }),
"Algorithm::Evolutionary::Individual::Vector", "Good ref" );
my $primitives = { sum => [2, -1, 1],
multiply => [2, -1, 1],
substract => [2, -1, 1],
divide => [2, -1, 1],
x => [0, -10, 10],
y => [0, -10, 10] };
( run in 0.454 second using v1.01-cache-2.11-cpan-0d8aa00de5b )