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 sidenote: 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 paradigmfree 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 yx=2 and y2x=11
# wrt x and y.
# Our 2 genes are 'x' and 'y'. We encode these as 8bit 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 = $current_fitness;
}
print "\nI tried to solve the system of equations: yx=2 and y2x=11.
+The solution should be x=3, y=5\n";
print "Final solution found: ".individual2string($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: yx=2 and y2
+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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