This produces the same results as your original, but 66 times faster. Which should make it about 12 times faster than your Java code.

Of course, if you fold many of the optimisations--which mostly come down to not doing the same thing multiple times--back into the Java code, that would run more quickly too. Iffy algorithms are iffy in any language.

#! perl -slw
use strict;
use List::Util qw[ sum ];
use Time::HiRes qw[ time ];

srand( 100 );

my $target          = 100;
my $population_size = 50;
my $chromosome_size = 100;
my $mutation_rate   = 0.1;
my $crossover_rate  = 0.7;
my @population      = ();
my %genome          = (
    '0001' => '1', '0010' => '2', '0011' => '3', '0100' => '4', '0101'
+ => '5',
    '0110' => '6', '0111' => '7', '1000' => '8', '1001' => '9',
    '1010' => '+', '1011' => '-', '1100' => '*', '1101' => '/'
);
my $gene_length = gene_length();

my $start = time;
initialise_population();
my $generation_counter = 1;
my $winner             = check_for_winner();
until ($winner) {
    @population = regenrate_population();
    $winner     = check_for_winner();
    $generation_counter++;
}
print("Solution reached in generation: $generation_counter");
print("The chromosome: @$winner");
print( get_phenotype($winner, $gene_length) . " = " . get_result($winn
+er)  );
printf "Took %.3f seconds\n", time()-$start;
exit;



sub regenrate_population {
    my @new_population = ();
    for ( 0 .. ( $population_size - 1 ) ) {
        $new_population[$_] = get_child( 
            get_nonrandom_chromosome(), 
            get_nonrandom_chromosome() 
        );
    }
    return (@new_population);
}

sub get_child {
    my( $chromosome1, $chromosome2 ) = @_;
    my $new_chromosome;
    if ( rand(1) < $crossover_rate ) {
        my $crossover_point = int( rand($chromosome_size) );
        $new_chromosome = [
            @{$chromosome1}[0..$crossover_point-1],
            @{$chromosome2}[$crossover_point..$chromosome_size-1],
        ];
    }
    else {
        $new_chromosome =  rand(1) > 0.5 ? $chromosome1 : $chromosome2
+;
    }

    $$new_chromosome[ rand($chromosome_size) ] ^= 0 if rand(1) < $muta
+tion_rate;
    return $new_chromosome;
}

sub check_for_winner {
    get_result($_) == $target and return $_ for @population
}

sub get_fitness_score {
    my $chromosome    = shift(@_);
    my $result        = get_result($chromosome);
    my $fitness_score =
      ( $target / ( $target + abs( $target - $result ) ) ) * 3;
}

sub get_nonrandom_chromosome {
    my @scores = map { get_fitness_score($_) } @population;
    my $rulet_position           = rand( sum @scores );
    my $temp_score               = 0;

    foreach my $i ( 0 .. $#scores ) {
        $temp_score += $scores[ $i ];
        return $population[ $i ] if $temp_score > $rulet_position;
    }
}

my %memo;
sub get_result {
    return $memo{ $_[0] } //= evalExpr( get_phenotype( $_[0], $gene_le
+ngth ) );
}

sub initialise_population {
    for my $chromosome ( 0 .. $population_size ) {
        for my $nucleotide ( 0 .. $chromosome_size ) {
            $population[$chromosome]->[$nucleotide] = int( rand(1) + 0
+.5 );
        }
    }
}

sub get_phenotype {
    my( $chromosome, $len ) = @_;

    my $ep = join'', map {
        $genome{ $_ } // '';
    } unpack "(a$len)*", join'', @$chromosome;

    $ep =~ s[^\D+][];
    $ep =~ s[(\d)(\d+)][$1]g;
    $ep =~ s[(\D)(\D+)][$1]g;
    $ep =~ s[\D+$][];
    return $ep;
}

sub gene_length {
    my $len = length( each %genome );
    while( my $key = each %genome ) {
        die "Invalid genotype" unless length( $key ) == $len;
    }
    return $len;
}

sub evalExpr {
     local $_ = shift;
     s[(?<=[^*/+-])([*/+-])][<$1>]g;
     1 while s[([^>]+)<([*/])>([^<]+)][$2 eq '*' ? $1 * $3 : $1 / $3]e
+;
     1 while s[([^>]+)<([+-])>([^<]+)][$2 eq '+' ? $1 + $3 : $1 - $3]e
+;

     return $_;
}

__END__
Solution reached in generation: 5
The chromosome: 0 0 0 0 1 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1 0 1 1 1 1 1 0 
+0 0 1 0 0 0 0 0 1 1 0 0 1 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0
+ 1 1 1 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 1 1 1 0 1 1 0 1 1 1 0 0 0 0 0 0 
+1 0 1 0
9*4*3-8 = 100
Took 7.711 seconds

Solution reached in generation: 5
The chromosome: 0 0 0 0 1 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1 0 1 1 1 1 1 0 
+0 0 1 0 0 0 0 0 1 1 0 0 1 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0
+ 1 1 1 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 1 1 1 0 1 1 0 1 1 1 0 0 0 0 0 0 
+1 0 1 0
9*4*3-8 = 100
Took 0.115 seconds
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