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Re: Genetic Programming or breeding Perls

by rtpc (Initiate)
on Aug 21, 2001 at 18:13 UTC ( #106571=note: print w/ replies, xml ) Need Help??


in reply to Genetic Programming or breeding Perls

Hhhmm, I've written my on vesion, with somewhat different operators (genes) to help with the speed of convergence. Actually, I changed the goal too, it now looks for an individual number, repedatively; it eventually prints ot JUST ANOTHER PERL HACKER. But I'm having a problem, quite often the populaion i overrun with exactly the same genes. I really need hlp figuring out how this is happening. The big differences are the choose and breed routines, which are called select and mate in this code. The mate routine selects some of the most fit oganisms, breeds them, and replaces some of the least fit organisms with the offspring. Here's the code:

#!/usr/bin/perl package GenePool; sub new { my $class = shift; my $self = {}; bless($self, $class); $self->{GENES} = [ '$x+=1;', '$y+=1;', '$X-=1;', '$y-=1;', '$X+=$y;', '$y+=$x;', '$x-=$y;', '$y-=$x;', ';' ]; return $self; } sub gene { my $self = shift; return ${$self->{GENES}}[rand(@{$self->{GENES}})]; } package Organism; sub new { my $class = shift; my $self = {}; bless($self, $class); $self->{LENGTH} = 12; $self->{GENES} = []; $self->{NEXTGEN} = []; return $self; } #create genes for this organism by selecting genes from the genepool sub initialize { my $self = shift; my $genepool = GenePool->new(); foreach (1..$self->{LENGTH}){ push(@{$self->{GENES}}, $genepool->gene()); } return; } sub set_nextgen { my $self = shift; @{$self->{NEXTGEN}} = @_; return; } sub age { my $self = shift; @{$self->{GENES}} = @{$self->{NEXTGEN}} unless $#{$self->{NEXTGEN} +} == -1; return; } sub get_genes { my $self = shift; return @{$self->{GENES}}; } #return code to be evaluated sub get_code { my $self = shift; my $code = ""; foreach(@{$self->{GENES}}){ $code .= $_; } return $code; } package Population; sub new { my ($class, $size) = @_; my $self = {}; bless($self, $class); $size++ if $size%2 == 0; $size+=2 if ($size+1)%4 != 0; $self->{SIZE} = $size; $self->{ORGANISMS} = []; $self->{FITNESSES} = []; $self->{OBJECTIVE} = ""; $self->{VERBOSE} = ""; $self->{MIDDLE} = 0; foreach (0..$self->{SIZE}){ my $organism = Organism->new(); $organism->initialize; push(@{$self->{ORGANISMS}}, $organism); } return $self; } sub set_verbosity { my $self = shift; $self->{VERBOSE} = shift; return; } sub get_verbosity { my $self = shift; return $self->{VERBOSE}; } sub set_objective { my $self = shift; $self->{OBJECTIVE} = shift; return; } sub get_objective { my $self = shift; return $self->{OBJECTIVE}; } #determine the fitnesses of each organism sub fitness { my $self = shift; my $f = $self->{FITNESSES}; my $i = 0; print STDERR "\nFITNESS OF GENEPOOL evaluation(code_value)\n" if $self +->{VERBOSE}; foreach my $organism (@{$self->{ORGANISMS}}){ my $val = eval('my $x = 1; my $y = 1;' . $organism->get_code() +); ${$f}[$i] = $self->evaluate($val); print STDERR ${$f}[$i] . "($val)" if $self->{VERBOSE}; $i++; } print STDERR "\n" if $self->{VERBOSE}; return; } sub evaluate { my ($self, $val) = @_; return -abs($self->{OBJECTIVE} - $val); } #scale the fitnesses so they are less than one and add to one sub scale_fitness { my $self = shift; my $fitnesses = $self->{FITNESSES}; my $i; my $min = ${$fitnesses}[0]; my $size = $self->{SIZE}; my $sum = 0.0; for ($i = 0; $i <= $size; ++$i ){ $min = ${$fitnesses}[$i] if ${$fitnesses}[$i] < $min; } for ( $i = 0; $i <= $size; ++$i ){ ${$fitnesses}[$i] -= $min; $sum += ${$fitnesses}[$i]; } for ( $i = 0; $i <= $size; ++$i ){ if($sum != 0){ ${$fitnesses}[$i] /= $sum; }else{ ${$fitnesses}[$i] = 1/$#{$fitnesses}; } } return; } #pick the fitest individual, excepting those we are told to ignore # (which are the previous picks) sub select { my ($self, $type, @excludelist) = @_; my $index = 0; my ($fitest, $ffit); $ffit = 1.0 if $type eq 'least fit'; foreach my $fitness (@{$self->{FITNESSES}}){ my $next = ""; foreach (@excludelist){ if($index == $_){ $next = "next"; last; }} $index++; next if $next eq "next"; if ( (($type eq 'fitest') && ($ffit <= $fitness)) || (($type eq 'least fit') && ($ffit >= $fitness)) ){ $fitest = $index - 1; $ffit = $fitness; } } return ${$self->{ORGANISMS}}[$fitest], $fitest; } sub find_middle { my $self = shift; my $f = 0; foreach my $fitness (@{$self->{FITNESSES}}){ $f += $fitness; } $self->{MIDDLE} = $f/(@{$self->{FITNESSES}}+1); return; } #find the individual who's fitness is nearest the "middle" sub find_nearest_middle { my $self = shift; my $middle = $self->{MIDDLE}; my ($nearest, $n, $i); foreach (@{$self->{FITNESSES}}){ if ( abs($nearest - $middle) > abs($_ - $middle)){ $nearest = $_; $n = $i; } $i++; } return ${$self->{ORGANISMS}}[$n]; } sub mutate { my @genes = @_; my $genepool = GenePool->new(); foreach my $i (0..$#genes){ $genes[$i] = $genepool->gene if rand(1.0) > 0.825; } return @genes; } #produce offspring for the next generation sub mate { my $self = shift; my $size = $self->{SIZE}; my @minexcludes = (); my @maxexcludes = (); for ( my $i = 0; $i < $size; $i+=4 ){ my $chance = rand(1.0); if($chance > 0.5){ my (@genes_one, @genes_two, $org_one, $org_two, $index_one +, $index_two); ($org_one, $index_one) = $self->select('fitest', @maxexclu +des); push(@maxexcludes, $index_one); @genes_one = $org_one->get_genes(); ($org_two, $index_two) = $self->select('fitest', @maxexclu +des); push(@maxexcludes, $index_two); @genes_two = $org_two->get_genes(); my @new_genes_one = @genes_one; my @new_genes_two = @genes_two; my $point = 1 + int(rand(@genes_one - 1)); splice @new_genes_one, $point; splice @new_genes_two, $point; push @new_genes_one, (splice @genes_two, $point); push @new_genes_two, (splice @genes_one, $point); my (undef, $min_one) = $self->select('least fit', @minexcl +udes); push(@minexcludes, $min_one); my (undef, $min_two) = $self->select('least fit', @minexcl +udes); push(@minexcludes, $min_two); ${$self->{ORGANISMS}}[$min_one]->set_nextgen(@new_genes_on +e); ${$self->{ORGANISMS}}[$min_two]->set_nextgen(@new_genes_tw +o); }elsif($chance < 0.05){ if(rand(1.0)>0.5){ my (@genes_one, $org_one, $index_one); ($org_one, $index_one) = $self->select('fitest', @maxe +xcludes); push(@maxexcludes, $index_one); @genes_one = $org_one->get_genes(); my @new_genes_one = @genes_one; @new_genes_one = mutate(@genes_one); my (undef, $min_one) = $self->select('least fit', @min +excludes); push(@minexcludes, $min_one); ${$self->{ORGANISMS}}[$min_one]->set_nextgen(@new_gene +s_one); }else{ my (@genes_two, $org_two, $index_two); ($org_two, $index_two) = $self->select('fitest', @maxe +xcludes); push(@maxexcludes, $index_two); @genes_two = $org_two->get_genes(); my @new_genes_two = @genes_two; @new_genes_two = mutate(@genes_two); my (undef, $min_two) = $self->select('least fit', @min +excludes); push(@minexcludes, $min_two); ${$self->{ORGANISMS}}[$min_two]->set_nextgen(@new_gene +s_two); } } } return; } sub generate { my $self = shift; foreach my $organism (@{$self->{ORGANISMS}}){ $organism->age(); } return; } package Statistics; sub new { my $class = shift; my $self = {}; bless($self, $class); $self->{GRADE} = [ ' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I +', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]; return $self; } sub grade_fitest { my ($self, $pop) = @_; my ($best, undef) = ${$pop->{ORGANISMS}}[$pop->select('fitest')]; my $indexorg = eval('my $x=1; my $y=1;' . $best->get_code()); my $index = $indexorg; my $obj = $pop->get_objective(); if($index < 0){ $index = abs($index) + 1 + $obj; } return "*", $indexorg if $index > 26; return ${$self->{GRADE}}[$index], $indexorg; } sub show_fitest { my ($self, $pop) = @_; my ($best, undef) = ${$pop->{ORGANISMS}}[$pop->select('fitest')]; return $best->get_code(); } sub show_middle { my ($self, $pop) = @_; $pop->find_middle; my $middle = $pop->find_nearest_middle(); my $indexorg = eval('my $x=1; my $y=1;' . $middle->get_code()); my $index = $indexorg; my $obj = $pop->get_objective(); if($index < 0){ $index = abs($index) + 1 + $obj; } return "*", $indexorg if $index > 26; return ${$self->{GRADE}}[$index], $indexorg; } package main; $|=1; my @string = (10, 21, 19, 20, 0, 14, 15, 20, 8, 5, 18, 0, 16, 5, 18, 1 +2, 0, 8, 1, 3, 11, 5, 18); my (@kings, @halloffame); foreach my $target (@string){ my $population = Population->new(49); $population->set_objective($target); #$population->set_verbosity(1); my $generation = 0; my $found = 0; while(!$found){ print "Generation[$generation]"; $population->fitness(); $population->scale_fitness(); my $statistics = Statistics->new(); my ($grade, $val) = $statistics->grade_fitest($population); my $fitest = $statistics->show_fitest($population); print join("", @kings) . $grade; $population->mate(); $population->generate(); if($val == $target){ print "\n$fitest\n"; $found++; push(@kings, $grade); push(@halloffame, $fitest); }elsif($population->get_verbosity){ my ($grade, $val) = $statistics->show_middle($population); print " ($grade, $val)"; } print "\n" unless $found; $generation++; } } print "\nHALL OF FAME\n", join("\n", @halloffame);


Comment on Re: Genetic Programming or breeding Perls
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Re: Re: Genetic Programming or breeding Perls
by gumpu (Friar) on Sep 02, 2001 at 13:34 UTC

    "But I'm having a problem, quite often the populaion i overrun with exactly the same genes. I really need hlp figuring out how this is happening."

    There can be a number of reasons for that. (1) Your population size is too small (GP works best with large populations) (2) Your mutation rate is too low (but your program it looks fine), or (3) Individuals with a low fitness have a too low probability to reproduce. If only the fitest individuals are allowed to reproduce they will take over the whole population. So weaker individuals have to have a chance to reproduce too. The probability for this depends on the population size. For a large population it can be low, for a small population is has to be high.

    Hope that helps

    Have Fun

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