crossover_uniform_nasa

在一个任意的天线方向图下

#!/usr/bin/perl -w

use POSIX;

$population_size = 1000;
$bit_mutation_probability = 0.01;
$nsegments = 5;
$nbits = $nsegments * 24;
$iterations = 500;

# Generate an initial population of random individuals. Each individual is
# represented by a 16-bit number.

for ($i = 0; $i < $population_size; $i++) {
# Generate a random number between 0 and 65535 (2^16) Store the individuals
# into an array (population) of hashes. The hash element that we initialise
# is called 'value'.
	
	for ($n = $nbits - 1; $n >= 0; $n--) {
		$population[$i]{'value'}[$n] = floor (0.5 + rand (1));
		print STDERR "$population[$i]{'value'}[$n]";
	}
	
	print STDERR "\n";
}

for ($N = 0; $N < $iterations; $N++) {
	print STDERR "Iteration: $N\n";
# Go through all individuals in the current population. Simulate and calculate MSE.

	$str = "";

	for ($i = 0; $i < $population_size; $i++) {
# Generate NEC files to calculate horizontal and vertical radiation patterns

		for ($j = 0; $j < $nbits; $j++) {
			$str .= $population[$i]{'value'}[$j];
		}

		$str .= ' '
	}

	`bits2nec_nasa_fast.m $str`;

	for ($i = 0; $i < $population_size; $i++) {
# Generate NEC files to calculate horizontal and vertical radiation patterns

# Simulate these using NEC
# Extract radiation patterns for calculation of MSE
# Calculate MSE (both horizontal and vertical)

		$str = sprintf ('nec2++ -i /tmp/horiz_%i.nec ; nec2plot /tmp/horiz_%i.out > /tmp/horiz_%i.dat ; calc_mse.m /tmp/horiz_%i.dat ~/reference/horizontal_reference.dat', $i, $i, $i, $i);

		$h_mse = `$str`;

		$str = sprintf ('nec2++ -i /tmp/vert_%i.nec ; nec2plot /tmp/vert_%i.out > /tmp/vert_%i.dat ; calc_mse.m /tmp/vert_%i.dat ~/reference/vertical_reference.dat', $i, $i, $i, $i);

		$v_mse = `$str`;

# Remove trailing newlines
		
		chomp $h_mse;
		chomp $v_mse;
		
#		print STDERR "Mean squared errors (h, v) = $h_mse, $v_mse\n";

# Store result in $population[$i]{'mse'}

		$population[$i]{'mse'} = $h_mse + $v_mse;
		print STDERR "Evaluated " . ($i + 1) . " of $population_size\n";
	}

	@sorted = sort { ${$a}{'mse'} <=> ${$b}{'mse'} } @population;
	
	$rank = 1;
	$new_pop_size = 0;
	
	print STDERR "Current lowest MSE = $sorted[0]{'mse'}\n";
	
	$average_mse = 0;
	
	foreach (@sorted) {
		${$_}{'rank'} = $rank++;
		${$_}{'rank fitness'} = 2 * ($population_size - ${$_}{'rank'}) / ($population_size - 1);
		${$_}{'copies'} = floor (${$_}{'rank fitness'} + rand (1));
#		print STDERR "${$_}{'mse'}, copies = ${$_}{'copies'}\n";

		$average_mse += ${$_}{'mse'};
		
		for ($j = 0; $j < ${$_}{'copies'}; $j++) {
			$intermediate[$new_pop_size++] = ${$_}{'value'};
		}
	}
	
#	foreach (@intermediate) {
#		@code = @$_;
		
#		foreach (@code) {
#			print $_
#		}
		
#		print "\n";
#	}

	$average_mse /= $population_size;

	for ($i = 0, $best_code = ""; $i < $nbits; $i++) {
		$best_code .= $sorted[0]{'value'}[$i];
	}

	print $N . "\t" . $average_mse . "\t" . $sorted[0]{'mse'} . "\t" . $sorted[$population_size - 1]{'mse'} . "\t" .  $best_code . "\n";
	
#	print STDERR "Intermediate population is now $new_pop_size\n";
	
	$i = 0;
	
	foreach (@intermediate) {		
		@partner1 = @$_;
		@partner2 = @{$intermediate[floor (rand ($new_pop_size))]};

#		foreach (@partner1) {
#			print $_
#		}
		
#		print "\n";

#		foreach (@partner2) {
#			print $_
#		}
		
#		print "\n";

# Now we have two individuals selected from this list; $_ and $partner. We
# need to combine these using a crossover method

# Generate random number

		for ($j = $nbits - 1; $j >= 0; $j--) {
			$selector = floor (0.5 + rand (1));
			$new_population[$i]{'value'}[$j] = ($selector & $partner1[$j]) + (~$selector & $partner2[$j]);
		}

		$i++;
	}
	
#	print "New population:\n\n";
	
#	foreach (@new_population) {
#		@code = @{${$_}{'value'}};

#		foreach (@code) {
#			print $_;
#		}

#		print "\n";
#	}

# Now randomly mutate bits in individuals

	for ($n = 0; $n < $new_pop_size; $n++) {
#		print STDERR ("Before: $new_population[$n]{'value'}, After: ");

# For each bit, there is a certain probability that the bit will be inverted

		for ($i = 0; $i < $nbits; $i++) {
			if (rand (1) < $bit_mutation_probability) {
				 $new_population[$n]{'value'}[$i] = 1 - $new_population[$n]{'value'}[$i];
			}
		}
	}

#	print "After mutation\n";

#	foreach (@new_population) {
#		@code = @{${$_}{'value'}};

#		foreach (@code) {
#			print $_;
#		}

#		print "\n";
#	}
	
	@population = @new_population;
	$population_size = $new_pop_size;
}