note
Masem
As an addition test case that I'm using, I've taken the concepts in [id://31147], and used them to develop the following code:
<CODE>
#!/usr/bin/perl -w
use strict;
use Algorithm::Genetic;
use Data::Dumper;
my @genes = qw{
$x+=1; $x=$y; $y=$x; $x|=$y; $x+=$y;
};
my $target = 100;
my $algo = new Algorithm::Genetic( {
FITNESS => \&fitness,
MUTATOR => \&mutate,
REAP_CRITERIA => sub { $_[ 0 ]->{ FITNESS } },
MUTATE_CRITERIA => sub { (10000-$_[ 0 ]->{ FITNESS } )**2 }
} );
my @initcode;
foreach ( 0..10 ) {
my @bits = map { int rand @genes } ( 0..10 );
$initcode[ $_ ] = \@bits;
};
$algo->init_population( @initcode );
for (1..100) {
print "GENERATION $_\n";
print "-------------\n";
print join "\n", map { eval_code( get_code( @$_ ) ).' : '.get_code( @$_ ) } reverse $algo->get_population();
print "\n";
$algo->process_generation();
print "\n";
}
sub mutate {
my @clone = @{ $_[0]->{ DATA } };
if ( int( rand() + 0.5 ) ) {
# mutate by switching a new op in
my $pos = int rand @clone;
my $newop = int rand @genes;
while ( $newop == $clone[ $pos ] ) {
$newop = int rand @genes;
}
$clone[ $pos ] = $newop;
} else {
# mutate by adding a new op in
push @clone, $genes[ int rand @genes ];
}
return \@clone;
}
sub fitness {
my $code = $_[0]->{ DATA };
# Calculate the fitness;
my $string = get_code( @$code );
my $calc = eval_code( $string );
return ( $calc - $target )**2;
}
sub get_code {
my $string = 'my $x = 1; my $y = 1; ';
$string = join '', $string, map { $genes[ $_ ] } @_;
return $string;
}
sub eval_code {
return eval( $_[0] );
}
</CODE>
<P>While probably not as robust as the original entry, the solutions I'm getting are converging to the target value even after 100 generations, so something is working right...
<P>
<HR>
<I>
Dr. Michael K. Neylon - <a href="mailto:mneylon-pm@masemware.com">mneylon-pm@masemware.com</a>
||
"You've left the lens cap of your mind on again, Pinky" - The Brain
</I>
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