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@ -1,8 +1,6 @@ |
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<html> |
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<head> |
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<script> |
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class Gene |
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{ |
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constructor(min, max, value) |
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@ -31,6 +29,11 @@ |
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{ |
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return new Gene(this.min, this.max, this.value); |
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} |
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makeRandomGene() |
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{ |
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return new Gene(this.min, this.max, Math.random()); |
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} |
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} |
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@ -54,10 +57,20 @@ |
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{ |
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this.genes[Math.round(Math.random() * (this.genes.length-1))].setValue(Math.random()); |
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} |
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createRandomChromosome() |
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{ |
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let geneAr = []; |
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for(let i = 0; i < this.genes.length; i++) |
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{ |
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geneAr.push(this.genes[i].makeRandomGene()); |
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} |
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return new Chromosome(geneAr); |
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} |
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} |
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const mate = function(father, mother) |
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const breed = function(father, mother) |
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{ |
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let son = new Chromosome(father.getGenes()); |
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let daughter = new Chromosome(mother.getGenes()); |
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@ -67,6 +80,48 @@ |
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let blendCoef = Math.random(); |
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blendGene(son.getGenes()[i], daughter.getGenes()[i], blendCoef); |
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} |
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return [son, daughter]; |
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}; |
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function predicateBy(prop) |
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{ |
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return function(a,b) |
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{ |
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var result; |
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if(a[prop] > b[prop]) |
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{ |
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result = 1; |
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} |
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else if(a[prop] < b[prop]) |
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{ |
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result = -1; |
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} |
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return result; |
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} |
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} |
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const naturalSelection = function(population, keepNumber, fitnessFunction) |
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{ |
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let fitnessArray = []; |
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for(let i = 0; i < population.length; i++) |
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{ |
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const fitness = fitnessFunction(population[i]); |
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console.log(fitness); |
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fitnessArray.push({fit:fitness, chrom: population[i]}); |
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} |
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fitnessArray.sort(predicateBy("fit")); |
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let survivors = []; |
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let bestFitness = fitnessArray[0].fit; |
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let bestChromosome = fitnessArray[0].chrom; |
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for(let i = 0; i < keepNumber; i++) |
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{ |
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survivors.push(fitnessArray[i].chrom); |
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} |
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return {survivors: survivors, bestFit: bestFitness, bestChrom: bestChromosome}; |
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}; |
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const blendGene = function(gene1, gene2, blendCoef) |
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@ -80,20 +135,115 @@ |
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gene2.setValue(value2); |
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}; |
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let gene1 = new Gene(1,10,.6); |
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let gene2 = new Gene(2,5,0.4); |
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const matePopulation = function(population, desiredPopulationSize) |
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{ |
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let pairsNeeded = (desiredPopulationSize - population.length)/2; |
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const originalLength = population.length; |
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for(let i = 0; i < pairsNeeded; i++) |
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{ |
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let index1 = Math.round(Math.random() * (originalLength-1)); |
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let index2 = Math.round(Math.random() * (originalLength-1)); |
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if(index1 !== index2) |
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{ |
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const babies = breed(population[index1], population[index2]); |
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population.push(babies[0]); |
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population.push(babies[1]); |
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} |
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} |
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}; |
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const mutatePopulation = function(population, mutatePercentage) |
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{ |
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if(population.length >= 2) |
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{ |
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let mutations = mutatePercentage * |
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population.length * |
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population[0].getGenes().length; |
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for(let i = 0; i < mutations; i++) |
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{ |
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population[i].mutate(); |
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} |
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} |
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else |
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{ |
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console.log("Error, population too small to mutate"); |
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} |
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}; |
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const newBlood = function(population, immigrationSize) |
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{ |
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for(let i = 0; i < immigrationSize; i++) |
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{ |
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let geneticChromosome = population[0]; |
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population.push(geneticChromosome.createRandomChromosome()); |
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} |
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}; |
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const basicCostFunction = function(chromosome) |
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{ |
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console.log(chromosome); |
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console.log((chromosome.getGenes()[0].getRealValue())); |
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return Math.abs(chromosome.getGenes()[0].getRealValue() - 6) + |
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Math.abs(chromosome.getGenes()[1].getRealValue() - 2); |
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}; |
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const createRandomPopulation = function(geneticChromosome, populationSize) |
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{ |
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let population = []; |
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for(let i = 0; i < populationSize; i++) |
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{ |
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population.push(geneticChromosome.createRandomChromosome()); |
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} |
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return population; |
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}; |
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const runGeneticOptimization = function(geneticChromosome, constFunction, |
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populationSize, maxGenerations, |
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desiredCost, mutationRate, keepNumber, |
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newBloodNumber) |
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{ |
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let population = createRandomPopulation(geneticChromosome, populationSize); |
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let generation = 0; |
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let bestCost = Number.MAX_VALUE; |
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let bestChromosome = geneticChromosome; |
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do |
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{ |
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matePopulation(population, populationSize); |
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newBlood(population, newBloodNumber); |
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mutatePopulation(population, mutationRate); |
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let generationResult = naturalSelection(population, keepNumber, constFunction); |
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if(bestCost > generationResult.bestFit) |
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{ |
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bestChromosome = generationResult.bestChrom; |
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bestCost = generationResult.bestFit; |
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} |
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population = generationResult.survivors; |
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generation++; |
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console.log("Generation " + generation + " Best Cost: " + bestCost); |
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console.log(generationResult); |
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}while(generation < maxGenerations && bestCost > desiredCost); |
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return bestChromosome; |
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}; |
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let gene1 = new Gene(1,10,10); |
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let gene2 = new Gene(1,10,0.4); |
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let geneList = [gene1, gene2]; |
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var c = new Chromosome(geneList); |
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var c2 = new Chromosome(c.getGenes()); |
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console.log(c.getGenes()); |
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console.log(c2.getGenes()); |
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c.mutate(); |
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c.mutate(); |
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console.log(c.getGenes()); |
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console.log(c2.getGenes()); |
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</script> |
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let exampleOrganism = new Chromosome(geneList); |
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runGeneticOptimization(exampleOrganism, basicCostFunction, 100, 50, 0.01, 0.3, 20, 10); |
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</script> |
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</head> |
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