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ga-worker.js
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ga-worker.js
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//postMessage('{"act":"debug","data":"message"}');
function Item(name,weight,cost,bound){
this.name = name;
this.weight = weight;
this.cost = cost;
this.bound = bound;
}
/**
This is the worker. It is used by a1.html to perform all the CPU-intensive
processing, so the GUI will remain responsive.
**/
var config;
var runTimeout = 0;
var stop_running = true;
var population;
var run;
var gen;
//this is the function that is called whenever the worker receives a message.
//based on the content of the message (event.data.act), do the appropriate action.
onmessage = function(event) {
var message = JSON.parse(event.data);
switch(message.act){
case 'pause':
stop_running = true;
if(runTimeout) clearTimeout(runTimeout);
break;
case 'init':
config = message.data;
run = 0;
runGA();
break;
}
}
function runGA(){
gen = 0;
//make initial random population
population = new Array();
for(var i = 0;i<config.popSize;){
var object = new Object();
object.chromosome = generate_chromosome();
object.fitness = 0;
if(insert_into_population(object,population))
i++;
}
stop_running = false;
iterGA();
}
function iterGA(){
//sort population by fitness
if(config.fitness_order == "desc")
population.sort( function (a,b) { return b.fitness-a.fitness });
else
population.sort( function (a,b) { return a.fitness-b.fitness });
if(gen > 0){
//report best so far
var message = new Object();
message.act = "generation";
message.data = {}
message.data.pop = population[population.length-1];
message.data.items = config.items;
postMessage(JSON.stringify(message));
}
//termination sat for run?
if(stop_running || config.maxGenerations == gen){
run++;
var message = new Object();
message.act = "answer";
message.data = {}
message.data.pop = population[population.length-1];
message.data.items = config.items;
postMessage(JSON.stringify(message));
if(!stop_running && run < config.maxRuns){
runTimeout = setTimeout(runGA, 150);
return true;
}
return true;
}
for(var i = 0;i<population.length;i++){
population[i].fitness = measure_fitness(population[i].chromosome);
}
var newPopulation = new Array();
for(var i = 0;i<population.length;){
var rnum = Math.ceil(Math.random() * 3);
switch(rnum){
case 1:
//select one individual based on fitness
var individual = population[select_from_population()];
//perform reproduction
var newIndividual = new Object();
newIndividual.chromosome = individual.chromosome.slice();
newIndividual.fitness = individual.fitness;
//insert copy in new pop
if(insert_into_population(newIndividual,newPopulation))
i++;
break;
case 2:
//select two individuals based on fitness
var individual1 = population[select_from_population()];
var individual2 = population[select_from_population()];
//perform crossover
var child1 = new Object();
var child2 = new Object();
var xover = Math.floor(Math.random()*individual1.chromosome.length);
if(config.unique_chromosomes){
var r = Math.random();
if(r < 0.5){
child1.chromosome = crossover1(individual1.chromosome,individual2.chromosome);
child2.chromosome = crossover1(individual1.chromosome,individual2.chromosome);
}else{
child1.chromosome = crossover2(individual1.chromosome,individual2.chromosome);
child2.chromosome = crossover2(individual1.chromosome,individual2.chromosome);
}
}else{
child1.chromosome = individual1.chromosome.slice(0,xover).concat(individual2.chromosome.slice(xover));
child2.chromosome = individual2.chromosome.slice(0,xover).concat(individual1.chromosome.slice(xover));
}
child1.fitness = measure_fitness(child1.chromosome);
child2.fitness = measure_fitness(child2.chromosome);
var candidates = new Array();
candidates.push(individual1);
candidates.push(individual2);
candidates.push(child1);
candidates.push(child2);
if(config.fitness_order == "desc")
candidates.sort( function (a,b) { return b.fitness-a.fitness });
else
candidates.sort( function (a,b) { return a.fitness-b.fitness });
//insert offspring in new pop
if(insert_into_population(candidates[2],newPopulation))
i++;
if(insert_into_population(candidates[3],newPopulation))
i++;
break;
case 3:
//select one individial based on fitness
var individual = population[select_from_population()];
//perform mutation
var mutant = new Object();
mutant.chromosome = individual.chromosome.slice();
var r = Math.random();
var x1 = Math.floor(Math.random()*mutant.chromosome.length);
var x2 = Math.floor(Math.random()*mutant.chromosome.length);
if(r < 0.5){
//Mutate 1 - reciprocal exchange
var temp = mutant.chromosome[x1];
mutant.chromosome[x1] = mutant.chromosome[x2];
mutant.chromosome[x2] = temp;
}else{
//Mutate 2 - insertion
var tempC = mutant.chromosome.splice(x1,1);
var tempA = mutant.chromosome.splice(x2);
mutant.chromosome = mutant.chromosome.concat(tempC.concat(tempA));
}
mutant.fitness = measure_fitness(mutant.chromosome);
//insert mutant in new pop
if(insert_into_population(mutant,newPopulation))
i++;
break;
default:
}
}
population = newPopulation;
gen++;
if(!stop_running){
runTimeout = setTimeout(iterGA, 50);
}
}
function crossover1(parent1, parent2){
//Order crossover
var A = Math.floor(Math.random()*parent1.length);
var B = Math.floor(Math.random()*parent1.length);
while(A == B)
B = Math.floor(Math.random()*parent1.length);
if(A > B){
var temp = B;
B = A;
A = temp;
}
var child = new Array(parent1.length);
//copy A to B from parent 1
for(var i = A;i<B;i++){
child[i] = parent1[i];
}
//fill in the rest of child with parent2's genes
var parent2_index = 0;
for(var child_index = 0;child_index<parent1.length;child_index++){
if(child[child_index] == undefined){
for(;parent2_index<parent1.length;parent2_index++){
//if(child.indexOf(parent2[parent2_index])<0){
child[child_index] = parent2[parent2_index];
break;
//}
}
}
}
return child;
}
function crossover2(parent1, parent2){
//Position-based crossover
var child = new Array(parent1.length);
for(var i = 0;i<parent1.length;i++){
var r = Math.random();
if(r < 0.5)
child[i] = parent1[i];
}
//fill in the rest of child with parent2's genes
var parent2_index = 0;
for(var child_index = 0;child_index<parent1.length;child_index++){
if(child[child_index] == undefined){
for(;parent2_index<parent1.length;parent2_index++){
//if(child.indexOf(parent2[parent2_index])<0){
child[child_index] = parent2[parent2_index];
break;
//}
}
}
}
return child;
}
function insert_into_population(individual,newPopulation){
//don't insert into population if child violates max weight rule
var total_weight = 0;
for(var i=0;i<individual.chromosome.length;i++){
total_weight += individual.chromosome[i].weight;
}
if(total_weight > config.max_weight)
return false;
//don't insert into population if child violates bound rule
for(var i=0;i<config.items.length;i++){
var countArray = individual.chromosome.filter(get_items_filter,config.items[i]);
if(countArray.length > config.items[i].bound){
return false;
}
}
newPopulation.push(individual);
return true;
}
function arrays_equal(array1,array2){
if(array1.length != array2.length)
return false;
for(var i=0;i<array1.length;i++){
if(array1[i] != array2[i])
return false;
}
return true;
}
function select_from_population(){
switch(config.selection){
case "rank":
var r = Math.random()*((population.length*(population.length+1))/2);
var sum = 0;
for(var i = 0;i<population.length;i++){
for (sum += i; sum > r; r++) return i;
}
return population.length-1;
break;
case "tournament":
var choices = new Array();
for(var i = 0;i<5;i++){
var rnum = Math.floor(Math.random() * population.length);
choices[i] = population[rnum];
choices[i].index = rnum;
}
if(config.fitness_order == "desc")
choices.sort( function (a,b) { return b.fitness-a.fitness });
else
choices.sort( function (a,b) { return a.fitness-b.fitness });
var r = Math.random();
//p = 0.5
if(r < 0.5){
//return most fit
return choices[choices.length-1].index;
}
//otherwise, return a random choice
var rnum = Math.floor(Math.random() * choices.length);
return choices[rnum].index;
break;
default:
return 1;
}
}
function measure_fitness(chromosome){
var fitness = 0;
for(var i = 0;i<chromosome.length;i++){
fitness+=chromosome[i].value;
}
return fitness;
}
function get_items_filter(item){
return this === item;
}
//randomly generate a string
function generate_chromosome() {
var randomchromosome = [];
var weight_so_far = 0;
var available_items = config.items.slice();
while(weight_so_far <= config.max_weight && available_items.length){
var index = Math.floor(Math.random() * available_items.length);
if((weight_so_far + available_items[index].weight) <= config.max_weight){
randomchromosome = randomchromosome.concat(available_items[index]);
weight_so_far += available_items[index].weight;
var countArray = randomchromosome.filter(get_items_filter,available_items[index]);
if(countArray.length >= available_items[index].bound){
available_items.splice(index,1);
}
}else{
//item is too big for knapsack, don't use it anymore
available_items.splice(index,1);
}
}
return randomchromosome;
}