AI-Genetic-Pro

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lib/AI/Genetic/Pro.pm  view on Meta::CPAN


Alias for C<crossProb>.

=item I<$ga>-E<gt>B<mutProb>()

This method is used to query and set the mutation rate.

=item I<$ga>-E<gt>B<mutation>()

Alias for C<mutProb>.

=item I<$ga>-E<gt>B<parents>($parents)

Set/get number of parents in a crossover.

=item I<$ga>-E<gt>B<init>($args)

This method initializes the population with random individuals/chromosomes. It MUST be called before any call to C<evolve()>. It expects one argument, which depends on the type of individuals/chromosomes:

=over 4

=item B<bitvector>

For bitvectors, the argument is simply the length of the bitvector.

    $ga->init(10);

This initializes a population where each individual/chromosome has 10 genes.

=item B<listvector>

For listvectors, the argument is an anonymous list of lists. The number of sub-lists is equal to the number of genes of each individual/chromosome. Each sub-list defines the possible string values that the corresponding gene can assume.

    $ga->init([
               [qw/red blue green/],
               [qw/big medium small/],
               [qw/very_fat fat fit thin very_thin/],
              ]);

This initializes a population where each individual/chromosome has 3 genes and each gene can assume one of the given values.

=item B<rangevector>

For rangevectors, the argument is an anonymous list of lists. The number of sub-lists is equal to the number of genes of each individual/chromosome. Each sub-list defines the minimum and maximum integer values that the corresponding gene can assume.

    $ga->init([
               [1, 5],
               [0, 20],
               [4, 9],
              ]);

This initializes a population where each individual/chromosome has 3 genes and each gene can assume an integer within the corresponding range.

=item B<combination>

For combination, the argument is an anonymous list of possible values of gene.

    $ga->init( [ 'a', 'b', 'c' ] );

This initializes a population where each chromosome has 3 genes and each gene
is a unique combination of 'a', 'b' and 'c'. For example genes looks something
like that:

    [ 'a', 'b', 'c' ]    # gene 1
    [ 'c', 'a', 'b' ]    # gene 2
    [ 'b', 'c', 'a' ]    # gene 3
    # ...and so on...

=back

=item I<$ga>-E<gt>B<evolve>($n)

This method causes the GA to evolve the population for the specified number of
generations. If its argument is 0 or C<undef> GA will evolve the population to
infinity unless a C<terminate> function is specified.

=item I<$ga>-E<gt>B<getHistory>()

Get history of the evolution. It is in a format listed below:

	[
		# gen0   gen1   gen2   ...          # generations
		[ max0,  max1,  max2,  ... ],       # max values
		[ mean,  mean1, mean2, ... ],       # mean values
		[ min0,  min1,  min2,  ... ],       # min values
	]

=item I<$ga>-E<gt>B<getAvgFitness>()

Get I<max>, I<mean> and I<min> score of the current generation. In example:

    my ($max, $mean, $min) = $ga->getAvgFitness();

=item I<$ga>-E<gt>B<getFittest>($n, $unique)

This function returns a list of the fittest chromosomes from the current
population.  You can specify how many chromosomes should be returned and if
the returned chromosomes should be unique. See example below.

    # only one - the best
    my ($best) = $ga->getFittest;

    # or 5 bests chromosomes, NOT unique
    my @bests = $ga->getFittest(5);

    # or 7 bests and UNIQUE chromosomes
    my @bests = $ga->getFittest(7, 1);

If you want to get a large number of chromosomes, try to use the
C<getFittest_as_arrayref> function instead (for efficiency).

=item I<$ga>-E<gt>B<getFittest_as_arrayref>($n, $unique)

This function is very similar to C<getFittest>, but it returns a reference 
to an array instead of a list. 

=item I<$ga>-E<gt>B<generation>()

Get the number of the current generation.

=item I<$ga>-E<gt>B<people>()

lib/AI/Genetic/Pro.pm  view on Meta::CPAN

Description of min line (default: I<Min value>).

=item -legend2

Description of min line (default: I<Mean value>).

=item -legend3

Description of min line (default: I<Max value>).

=item -width

Width of a chart (default: I<640>).

=item -height

Height of a chart (default: I<480>).

=item -font

Path to font (in *.ttf format) to be used (default: none).

=item -logo

Path to logo (png/jpg image) to embed in a chart (default: none).

=item For example:

	$ga->chart(-width => 480, height => 320, -filename => 'chart.png');

=back

=item I<$ga>-E<gt>B<save>($file)

Save the current state of the genetic algorithm to the specified file.

=item I<$ga>-E<gt>B<load>($file)

Load a state of the genetic algorithm from the specified file. 

=item I<$ga>-E<gt>B<as_array>($chromosome)

In list context return an array representing the specified chromosome. 
In scalar context return an reference to an array representing the specified 
chromosome. If I<variable_length> is turned on and is set to level 2, an array 
can have some C<undef> values. To get only C<not undef> values use 
C<as_array_def_only> instead of C<as_array>.

=item I<$ga>-E<gt>B<as_array_def_only>($chromosome)

In list context return an array representing the specified chromosome. 
In scalar context return an reference to an array representing the specified 
chromosome. If I<variable_length> is turned off, this function is just an
alias for C<as_array>. If I<variable_length> is turned on and is set to 
level 2, this function will return only C<not undef> values from chromosome. 
See example below:

    # -variable_length => 2, -type => 'bitvector'
	
    my @chromosome = $ga->as_array($chromosome)
    # @chromosome looks something like that
    # ( undef, undef, undef, 1, 0, 1, 1, 1, 0 )
	
    @chromosome = $ga->as_array_def_only($chromosome)
    # @chromosome looks something like that
    # ( 1, 0, 1, 1, 1, 0 )

=item I<$ga>-E<gt>B<as_string>($chromosome)

Return a string representation of the specified chromosome. See example below:

	# -type => 'bitvector'
	
	my $string = $ga->as_string($chromosome);
	# $string looks something like that
	# 1___0___1___1___1___0 
	
	# or 
	
	# -type => 'listvector'
	
	$string = $ga->as_string($chromosome);
	# $string looks something like that
	# element0___element1___element2___element3...

Attention! If I<variable_length> is turned on and is set to level 2, it is 
possible to get C<undef> values on the left side of the vector. In the returned
string C<undef> values will be replaced with B<spaces>. If you don't want
to see any I<spaces>, use C<as_string_def_only> instead of C<as_string>.

=item I<$ga>-E<gt>B<as_string_def_only>($chromosome)

Return a string representation of specified chromosome. If I<variable_length> 
is turned off, this function is just alias for C<as_string>. If I<variable_length> 
is turned on and is set to level 2, this function will return a string without
C<undef> values. See example below:

	# -variable_length => 2, -type => 'bitvector'
	
	my $string = $ga->as_string($chromosome);
	# $string looks something like that
	#  ___ ___ ___1___1___0 
	
	$string = $ga->as_string_def_only($chromosome);
	# $string looks something like that
	# 1___1___0 

=item I<$ga>-E<gt>B<as_value>($chromosome)

Return the score of the specified chromosome. The value of I<chromosome> is 
calculated by the fitness function.

=back

=head1 SUPPORT

C<AI::Genetic::Pro> is still under development; however, it is used in many
production environments.

=head1 TODO

=over 4

=item Examples.

=item More tests.

=item More warnings about incorrect parameters.

=back

=head1 REPORTING BUGS

When reporting bugs/problems please include as much information as possible.
It may be difficult for me to reproduce the problem as almost every setup
is different.

A small script which yields the problem will probably be of help. 

=head1 THANKS

Mario Roy for suggestions about efficiency.

Miles Gould for suggestions and some fixes (even in this documentation! :-).

Alun Jones for fixing memory leaks.

Tod Hagan for reporting a bug (rangevector values truncated to signed  8-bit quantities) and supplying a patch.

Randal L. Schwartz for reporting a bug in this documentation.

Maciej Misiak for reporting problems with C<combination> (and a bug in a PMX strategy).

LEONID ZAMDBORG for recommending the addition of variable-length chromosomes as well as supplying relevant code samples, for testing and at the end reporting some bugs.

Christoph Meissner for reporting a bug.

Alec Chen for reporting some bugs.

=head1 AUTHOR

Strzelecki Lukasz <lukasz@strzeleccy.eu>

=head1 SEE ALSO



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