我正在实施遗传算法。我从这里上课，但我不明白如何为这些课程提供输入。我有动态输入。我有三个需要优化的输入。如果有人知道，请给我提供指导。
这是代码
<?php
class GA {
var $population; //Objects array (same classes)
var $fitness_function; //The fitness function name (string)
var $crossover_functions; //The crossover function name (string) or array
var $mutation_function; //The mutation function name (string)
var $mutation_rate; //Mutation rate per child (%)
var $generations; //Number of generations
var $num_couples; //Number of couples for each generation
var $death_rate; //Number of killed objects for each generation
function crossover($parent1,$parent2,$cross_functions)
{
$class = get_class($parent1);
if ($class != get_class($parent2))
return false;
if (!is_array($cross_functions)) {
$cross_function = $cross_functions;
$cross_functions = array();
}
$child = new $class();
$properties = get_object_vars($parent1);
foreach ($properties as $propertie => $value)
{
if ($cross_function) $cross_functions[$propertie] = $cross_function;
if (function_exists($cross_functions[$propertie]))
$child->$propertie = $cross_functions[$propertie]($parent1->$propertie,$parent2->$propertie);
}
return $child;
}
function mutate(&$object,$mutation_function) {
$properties = get_object_vars($object);
foreach ($properties as $propertie => $value) {
$object->$propertie = $mutation_function($object->$propertie);
}
}
function fitness($object,$fitness_function) {
return $fitness_function($object);
}
//PRIVATE
function best($a, $b) {
if ($a[1] == $b[1]) return 0;
return ($a[1] < $b[1]) ? 1 : -1;
}
function select($objects,$fitness_function,$n=2) {
foreach ($objects as $object) {
$selection[] = array($object,$fitness_function($object));
}
usort($selection,array("GA", "best"));
$selection = array_slice($selection,0,$n);
foreach ($selection as $selected) {
$winners[] = $selected[0];
}
return $winners;
}
//PRIVATE
function worst($a, $b) {
if ($a[1] == $b[1]) return 0;
return ($a[1] < $b[1]) ? -1 : 1;
}
function kill(&$objects,$fitness_function,$n=2) {
foreach ($objects as $object) {
$selection[] = array($object,$fitness_function($object));
}
usort($selection,array("GA", "worst"));
$selection = array_slice($selection,0,count($selection)-$n);
$objects = array();
foreach ($selection as $selected) {
$objects[] = $selected[0];
}
}
//PRIVATE
function mass_crossover($objects,$cross_functions) {
foreach ($objects as $object) {
if (!$obj1) $obj1 = $object;
else {
$children[] = $this->crossover($obj1,$object,$this->crossover_functions);
$obj1 = null;
}
}
return $children;
}
//PRIVATE
function mass_mutation(&$objects) {
foreach($objects as $key => $object) {
if (rand(1,100) <= $this->mutation_rate) $this->mutate($objects[$key],$this->mutation_function);
}
}
function evolve() {
for ($i=0;$i<$this->generations;$i++) {
$couples = $this->select($this->population,$this->fitness_function,2*min($this->num_couples,floor(count($this->population)/2)));
$children = $this->mass_crossover($couples,$this->crossover_functions);
$this->mass_mutation($children);
$this->population = array_merge($this->population,$children);
$this->kill($this->population,$this->fitness_function,min($this->death_rate,count($this->population)-2));
}
}
}
?>
这里 gaexample2.php
<?php
require_once('ga.php');
class Human {
var $strength;
var $dexterity;
var $resistance;
var $intelligence;
function Human($strength=0,$dexterity=0,$resistance=0,$intelligence=0)
{
$this->strength = $strength;
$this->dexterity = $dexterity;
$this->resistance = $resistance;
$this->intelligence = $intelligence;
}
}
function debug($x) {
echo "<pre style='border: 1px solid black'>";
print_r($x);
echo '</pre>';
}
//This will be the mutation function. Just increments the property.
function inc($x) {
return $x+1;
}
//This will be the crossover function. Is just the average of all properties.
function avg($a,$b) {
return round(($a+$b)/2);
}
//This will be the fitness function. Is just the sum of all properties.
function total($obj) {
return $obj->strength + $obj->dexterity + $obj->resistance + $obj->intelligence;
}
$adam = new Human(4,2,3,1);
$eve = new Human(1,4,2,3);
$ga = new GA();
$ga->population = array($adam,$eve);
debug($ga->population);
$ga->fitness_function = 'total'; //Uses the 'total' function as fitness function
$ga->num_couples = 1; //4 couples per generation (when possible)
$ga->death_rate = 0; //No kills per generation
$ga->generations = 100; //Executes 100 generations
$ga->crossover_functions = 'avg'; //Uses the 'avg' function as crossover function
$ga->mutation_function = 'inc'; //Uses the 'inc' function as mutation function
$ga->mutation_rate = 10; //10% mutation rate
$ga->evolve(); //Run
debug($ga->population);
debug(GA::select($ga->population,'total',1)); //The best
?>
有人可以帮助显示每个步骤的单独输出吗