crossover_2p

在一个任意的天线方向图下

#!/usr/bin/perl -w

use POSIX;

$population_size = 20;
$bit_mutation_probability = 0.01;

# 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'.
	$population[$i]{'value'} = int (rand (65536));
}

$N = 0;

while (1) {
	print STDERR "Iteration: $N\n";
# Go through all individuals in the current population. Simulate and calculate MSE.

	for ($i = 0; $i < $population_size; $i++) {
# Determine dimensions of left and right dipole segments

		$right = $population[$i]{'value'} & 0x00FF;
		$left = $population[$i]{'value'} >> 8;

#		print STDERR "Dimensions: $left, $right\n";

# Generate NEC files to calculate horizontal and vertical radiation patterns

		`bits2nec.m horiz $left $right > /tmp/horiz.nec`;
		`bits2nec.m vert $left $right > /tmp/vert.nec`;

# Simulate these using NEC

		`nec2++ -i /tmp/horiz.nec`;
		`nec2++ -i /tmp/vert.nec`;

# Extract radiation patterns for calculation of MSE

		`nec2plot /tmp/horiz.out > /tmp/horiz.dat`;
		`nec2plot /tmp/vert.out > /tmp/vert.dat`;
		
# Calculate MSE (both horizontal and vertical)

		$h_mse = `calc_mse.m /tmp/horiz.dat ~/reference/horizontal_reference.dat`;
		$v_mse = `calc_mse.m /tmp/vert.dat ~/reference/vertical_reference.dat`;

# 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'};
		}
	}

	$average_mse /= $population_size;

	print $N . "\t" . $average_mse . "\t" . $sorted[0]{'mse'} . "\t" . $sorted[$population_size - 1]{'mse'} . "\n";
	
#	print STDERR "Intermediate population is now $new_pop_size\n";
	
	$i = 0;
	
	foreach (@intermediate) {		
		$partner = $intermediate[floor (rand ($new_pop_size))];
		print STDERR "$_ paired with $partner\n";
		
# So now we have two individuals selected from this list; $_ and $partner.
# We need to combine these using a crossover method

		$crossover_point_2 = 1 + floor (rand (15));
		$crossover_point_1 = floor (rand ($crossover_point_2 - 1));
		$mask_1 = 0xFFFF >> $crossover_point_1;
		$mask_2 = 0xFFFF >> $crossover_point_2;

		$front = $_ & ~$mask_1;
		$back = $_ & $mask_2;

		$new_population[$i]{'value'} = $front + $back + ($partner & ($mask_1 & ~$mask_2));

		print STDERR "Crossover at $crossover_point_1, $crossover_point_2\n";
		
		print STDERR "Child = $new_population[$i]{'value'}\n";
		$i++;
	}

	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 < 16; $i++) {
			if (rand (1) < $bit_mutation_probability) {
				$new_population[$n]{'value'} ^= (1 << $i);
			}
		}

		print STDERR ("$new_population[$n]{'value'}\n");
	}

	@population = @new_population;
	$population_size = $new_pop_size;

	$N++;
}