AI-Genetic
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bitvector => 'AI::Genetic::IndBitVector',
rangevector => 'AI::Genetic::IndRangeVector',
listvector => 'AI::Genetic::IndListVector',
);
##################
# sub new():
# This is the constructor. It creates a new AI::Genetic
# object. Options are:
# -population: set the population size
# -crossover: set the crossover probability
# -mutation: set the mutation probability
# -fitness: set the fitness function
# -type: set the genome type. See docs.
# -terminate: set termination sub.
sub new {
my ($class, %args) = @_;
my $self = bless {
ADDSEL => {}, # user-defined selections
ADDCRS => {}, # user-defined crossovers
ADDMUT => {}, # user-defined mutations
ADDSTR => {}, # user-defined strategies
} => $class;
my $self = shift;
if (@_) {
$self->{PEOPLE} = shift;
$self->{SORTED} = 0;
}
$self->{PEOPLE};
}
# useful little methods to set/query parameters.
sub size { $_[0]{POPSIZE} = $_[1] if defined $_[1]; $_[0]{POPSIZE} }
sub crossProb { $_[0]{CROSSRATE} = $_[1] if defined $_[1]; $_[0]{CROSSRATE} }
sub mutProb { $_[0]{MUTPROB} = $_[1] if defined $_[1]; $_[0]{MUTPROB} }
sub indType { $_[0]{INDIVIDUAL} }
sub generation { $_[0]{GENERATION} }
# sub inject():
# This method is used to add individuals to the current population.
# The point of it is that sometimes the population gets stagnant,
# so it could be useful add "fresh blood".
B<PLEASE NOTE:> As of v0.02, AI::Genetic has been re-written from
scratch to be more modular and expandable. To achieve this, I had
to modify the API, so it is not backward-compatible with v0.01.
As a result, I do not plan on supporting v0.01.
I will not go into the details of GAs here, but here are the
bare basics. Plenty of information can be found on the web.
In a GA, a population of individuals compete for survival. Each
individual is designated by a set of genes that define its
behaviour. Individuals that perform better (as defined by the
fitness function) have a higher chance of mating with other
individuals. When two individuals mate, they swap some of
their genes, resulting in an individual that has properties
from both of its "parents". Every now and then, a mutation
occurs where some gene randomly changes value, resulting in
a different individual. If all is well defined, after a few
generations, the population should converge on a "good-enough"
solution to the problem being tackled.
to fitness, returning an anonymous list of the sorted individuals.
=item I<$ga>-E<gt>B<people>()
Returns an anonymous list of individuals of the current population.
B<IMPORTANT>: the actual array reference used by the AI::Genetic object
is returned, so any changes to it will be reflected in I<$ga>.
=item I<$ga>-E<gt>B<size>(?I<newSize>?)
This method is used to query and set the population size.
=item I<$ga>-E<gt>B<crossProb>(?I<newProb>?)
This method is used to query and set the crossover rate.
=item I<$ga>-E<gt>B<mutProb>(?I<newProb>?)
This method is used to query and set the mutation rate.
=item I<$ga>-E<gt>B<indType>()
This method returns the type of individual: I<bitvector>, I<listvector>,
or I<rangevector>.
=item I<$ga>-E<gt>B<generation>()
This method returns the current generation.
Genetic/Individual.pm view on Meta::CPAN
my $self = shift;
return $self->{SCORE} if $self->{CALCED};
$self->{SCORE} = $self->{FITFUNC}->(scalar $self->genes);
$self->{CALCED} = 1;
return $self->{SCORE};
}
sub resetScore { $_[0]{CALCED} = 0 }
# hmmm .. how do I reset {CALCED} in case of mutation?
__END__
=head1 NAME
AI::Genetic::Individual - Base class for AI::Genetic Individuals.
=head1 SYNOPSIS
See L<AI::Genetic>.
Genetic/Individual.pm view on Meta::CPAN
If called as an instance method, it expects one argument
which defines the genes of the individual. All other attributes, like
fitness function, class, etc, will be copied from the calling
instance.
If called as a class method, then it calls I<newSpecific()>. See below
for details.
=item I<$individual>-E<gt>B<fitness(?anon_sub?)>
This method is used to set/query the anonymous subroutine used to
calculate the individual's fitness. If an argument is given, it expects
a subroutine reference which will be set as the fitness subroutine. In
either case, it'll return the fitness sub ref.
=item I<$individual>-E<gt>B<score()>
This method returns the fitness score of the individual. If the score has
never been calculated before, then the fitness function is executed and
the score saved. Subsequent calls to score() will return the cached value.
=item I<$individual>-E<gt>B<resetScore()>
This method resets the score of the individual such that a subsequent call
to I<score()> will result in the execution of the fitness sub.
=back
The following methods are meant to be over-ridden by any class that
inherits from AI::Genetic::Individual:
=over 4
=item I<$individual>-E<gt>B<newRandom(options)>
PLEASE NOTE: As of v0.02, AI::Genetic has been re-written from scratch
to be more modular and expandable. To achieve this, I had to modify the
API, so it is not backward-compatible with v0.01. As a result, I do not
plan on supporting v0.01.
I will not go into the details of GAs here, but here are the bare
basics. Plenty of information can be found on the web.
In a GA, a population of individuals compete for survival. Each
individual is designated by a set of genes that define its behaviour.
Individuals that perform better (as defined by the fitness function)
have a higher chance of mating with other individuals. When two
individuals mate, they swap some of their genes, resulting in an
individual that has properties from both of its "parents". Every now and
then, a mutation occurs where some gene randomly changes value,
resulting in a different individual. If all is well defined, after a few
generations, the population should converge on a "good-enough" solution
to the problem being tackled.
A GA implementation runs for a discrete number of time steps called
Given an anonymous list of individuals, this method sorts them
according to fitness, returning an anonymous list of the sorted
individuals.
*$ga*->people()
Returns an anonymous list of individuals of the current population.
IMPORTANT: the actual array reference used by the AI::Genetic object
is returned, so any changes to it will be reflected in *$ga*.
*$ga*->size(?*newSize*?)
This method is used to query and set the population size.
*$ga*->crossProb(?*newProb*?)
This method is used to query and set the crossover rate.
*$ga*->mutProb(?*newProb*?)
This method is used to query and set the mutation rate.
*$ga*->indType()
This method returns the type of individual: *bitvector*,
*listvector*, or *rangevector*.
*$ga*->generation()
This method returns the current generation.
FITNESS FUNCTION
Very quickly you will realize that properly defining the fitness
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