At the end all these languages are Turing complete and will eventually arrive at the same tape square. What interests me is the spirit and the inspiration. Pretty metaphysical I agree... A comment to your side-note: I am not sure what is more "scary": genes dying out or ideas? Ideas I should think.

I have whipped up some code to get me started with Perl's evolutionary computing toolkit.

#!/usr/bin/env perl

# Brief and dirty attempt to solve a system of simultaneous equations 
+(2)
# using Genetic Algorithms in particular CPAN module
#   Algorithm::Evolutionary
#   Perl module for performing paradigm-free evolutionary algorithms
#   by J. J. Merelo, jmerelo (at) geneura.ugr.es
# (parts of my program were copied from manpage)
#
# The toy problem here is to find INTEGER solutions to the system of e
+quations y-x=2 and y-2x=11
# wrt x and y.
# Our 2 genes are 'x' and 'y'. We encode these as 8-bit integers
# 7+1sign bit. The algorithm will mutate/crossover etc the bit string 
+of each member of
# the population. Then it will evaluate how well the genes of each mem
+ber of the population
# solve the problem at hand. This is called the fitness. The fittest g
+enes survive and the
# rest are discarded, with some probability.
# Author: bliako
# Date: 16/04/2018

use strict;
use warnings;

use Algorithm::Evolutionary::Experiment;
use Algorithm::Evolutionary::Op::Easy;
use Algorithm::Evolutionary::Op::Bitflip;
use Algorithm::Evolutionary::Op::Crossover;

my $num_genes = 2;

my $fitness = sub {
    my $individual = shift;
    my $genes = chromosome2genes($individual->Chrom());
    return calculate_discrepancy($genes);
};

my $m = Algorithm::Evolutionary::Op::Bitflip->new(2); # flip this numb
+er of bits randomly
my $c = Algorithm::Evolutionary::Op::Crossover->new(2); # crossover wi
+th 2 points
# every iteration applies the above operations to the population along
+ with a fitness function
# and selection rate (prob of good genes to survive, lower means more 
+"bad" genes enter the next generation) 
my $ez = new Algorithm::Evolutionary::Op::Easy $fitness, 0.4, [$m,$c];
my $popSize = 500; # population size, each individual in this pop has 
+a chromosome which consists of 2 genes
my $indiType = 'BitString'; # the chromosome is a sequence of bits as 
+a string
my $indiSize = 8*$num_genes; # 8 bits per gene
my $e = new Algorithm::Evolutionary::Experiment $popSize, $indiType, $
+indiSize, $ez;
my $populationRef;
my $previous_fitness = 0;
my ($current_fitness, $best);
while(1){
    $populationRef = $e->go();
    $best = $populationRef->[0];
    print "Best so far: ", $best->asString(), " (", individual2string(
+$best),")\n";
    $current_fitness = $best->Fitness();
    if( $current_fitness == 0 ){ print "bingo!\n"; last }
    #if( ($previous_fitness - $current_fitness) == 0 ){ last }
    $previous_fitness = $current_fitness;
}

print "\nI tried to solve the system of equations: y-x=2 and y-2x=11. 
+The solution should be x=3, y=5\n";
print "Final solution found: ".individual2string($best)."\n";

exit(0);

sub individual2string {
    my $individual = $_[0];
    my $genes = chromosome2genes($individual->Chrom());
    my $fit = calculate_discrepancy($genes);
    return genes2string($genes) . " -> discrepancy=" . $fit
}
# interpret an array of genes wrt our problem, i.e. an x and a y
sub genes2string {
    my $genes = $_[0];
    return "x=".$genes->[0].", y=".$genes->[1];
}
# convert a huge bit string into an array of genes
# the array to place the genes in is given
sub chromosome2genes {
    my $achromosome = $_[0]; # chromosome bit string containing all ge
+nes as 10101

    my @retgenes = (0)x$num_genes;

    # convert a chromosome which consists of genes which consist of bi
+ts(alleles)
    # into a set of numbers to be applied to our problem.
    # each chromosome below consists of 2 genes which consist of 8 bit
+s (1sign+7)
    # these 8bits are interpreted as integers in +-127 range (which is
+ enough for our problem
    # however if solution involved bigger numbers we need to increase 
+range/bits)
    my $i=0;
    while( $achromosome =~ /([01])([01]{7})/g ){
        my $sig = $1 eq '1' ? -1 : 1;
        my $g2 = $2;

        # Here is how a sequence of 8bits is converted to integers. 1s
+t bit is sign.
        # I am sure there is a better way using pack.
        my $g = 0;
        my $j = 1;
        map { $g += $_*$j; $j*=2; } split(//, $g2);
        $g *= $sig;

        $retgenes[$i++] = $g;
        #print "$g2->num=$g\n";
    }
    return \@retgenes
}
sub    calculate_discrepancy {
    my $genes = $_[0];

    # Our problem is to solve the simultaneous equation: y-x=2 and y-2
+x=11
    # where genes[0] -> y, genes[1]->x
    my $e1 = $genes->[0] - $genes->[1] - 2;
    my $e2 = $genes->[0] + 2*$genes->[1] - 11;
    # we calculate discrepancy but we need to return fitness:
    return -($e1*$e1 + $e2*$e2);
}

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In reply to Re^3: Curious about Perl's strengths in 2018 by bliako
in thread Curious about Perl's strengths in 2018 by Crosis

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