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class Gene
{
/**
* Constructs a new Gene to store in a chromosome.
* @param min minimum value that this gene can store
* @param max value this gene can possibly be
* @param value normalized value
*/
constructor(min, max, value)
{
this.min = min;
this.max = max;
this.value = value;
}
/**
* De-normalizes the value of the gene
* @returns {*}
*/
getRealValue()
{
return (this.max - this.min) * this.value + this.min;
}
getValue()
{
return this.value;
}
setValue(val)
{
this.value = val;
}
makeClone()
{
return new Gene(this.min, this.max, this.value);
}
makeRandomGene()
{
return new Gene(this.min, this.max, Math.random());
}
}
class Chromosome
{
/**
* Constructs a chromosome by making a copy of
* a list of genes.
* @param geneArray
*/
constructor(geneArray)
{
this.genes = [];
for(let i = 0; i < geneArray.length; i++)
{
this.genes.push(geneArray[i].makeClone());
}
}
getGenes()
{
return this.genes;
}
/**
* Mutates a random gene.
*/
mutate()
{
this.genes[Math.round(Math.random() * (this.genes.length-1))].setValue(Math.random());
}
/**
* Creates a totally new chromosome with same
* genetic structure as this chromosome but different
* values.
* @returns {Chromosome}
*/
createRandomChromosome()
{
let geneAr = [];
for(let i = 0; i < this.genes.length; i++)
{
geneAr.push(this.genes[i].makeRandomGene());
}
return new Chromosome(geneAr);
}
}
/**
* Mates two chromosomes using the blending method
* and returns a list of 2 offspring.
* @param father
* @param mother
* @returns {Chromosome[]}
*/
const breed = function(father, mother)
{
let son = new Chromosome(father.getGenes());
let daughter = new Chromosome(mother.getGenes());
for(let i = 0;i < son.getGenes().length; i++)
{
let blendCoef = Math.random();
blendGene(son.getGenes()[i], daughter.getGenes()[i], blendCoef);
}
return [son, daughter];
};
/**
* Blends two genes together based on a random blend
* coefficient.
**/
const blendGene = function(gene1, gene2, blendCoef)
{
let value1 = (blendCoef * gene1.getValue()) +
(gene2.getValue() * (1- blendCoef));
let value2 = ((1-blendCoef) * gene1.getValue()) +
(gene2.getValue() * blendCoef);
gene1.setValue(value1);
gene2.setValue(value2);
};
/**
* Helper function to sort an array
*
* @param prop name of JSON property to sort by
* @returns {function(*, *): number}
*/
function predicateBy(prop)
{
return function(a,b)
{
var result;
if(a[prop] > b[prop])
{
result = 1;
}
else if(a[prop] < b[prop])
{
result = -1;
}
return result;
}
}
/**
* Function which computes the fitness of everyone in the
* population and returns the most fit survivors. Method
* known as elitism.
*
* @param population
* @param keepNumber
* @param fitnessFunction
* @returns {{average: number,
* survivors: Array, bestFit: Chromosome }}
*/
const naturalSelection = function(population, keepNumber, fitnessFunction)
{
let fitnessArray = [];
let total = 0;
for(let i = 0; i < population.length; i++)
{
const fitness = fitnessFunction(population[i]);
console.log(fitness);
fitnessArray.push({fit:fitness, chrom: population[i]});
total+= fitness;
}
fitnessArray.sort(predicateBy("fit"));
let survivors = [];
let bestFitness = fitnessArray[0].fit;
let bestChromosome = fitnessArray[0].chrom;
for(let i = 0; i < keepNumber; i++)
{
survivors.push(fitnessArray[i].chrom);
}
return {average: total/population.length, survivors: survivors, bestFit: bestFitness, bestChrom: bestChromosome};
};
/**
* Randomly everyone in the population
*
* @param population
* @param desiredPopulationSize
*/
const matePopulation = function(population, desiredPopulationSize)
{
const originalLength = population.length;
while(population.length < desiredPopulationSize)
{
let index1 = Math.round(Math.random() * (originalLength-1));
let index2 = Math.round(Math.random() * (originalLength-1));
if(index1 !== index2)
{
const babies = breed(population[index1], population[index2]);
population.push(babies[0]);
population.push(babies[1]);
}
}
};
/**
* Randomly mutates the population
**/
const mutatePopulation = function(population, mutatePercentage)
{
if(population.length >= 2)
{
let mutations = mutatePercentage *
population.length *
population[0].getGenes().length;
for(let i = 0; i < mutations; i++)
{
population[i].mutate();
}
}
else
{
console.log("Error, population too small to mutate");
}
};
/**
* Introduces x random chromosomes to the population.
* @param population
* @param immigrationSize
*/
const newBlood = function(population, immigrationSize)
{
for(let i = 0; i < immigrationSize; i++)
{
let geneticChromosome = population[0];
population.push(geneticChromosome.createRandomChromosome());
}
};
let costx = Math.random() * 10;
let costy = Math.random() * 10;
/** Defines the cost as the "distance" to a 2-d point.
* @param chromosome
* @returns {number}
*/
const basicCostFunction = function(chromosome)
{
return Math.abs(chromosome.getGenes()[0].getRealValue() - costx) +
Math.abs(chromosome.getGenes()[1].getRealValue() - costy);
};
/**
* Creates a totally random population based on a desired size
* and a prototypical chromosome.
*
* @param geneticChromosome
* @param populationSize
* @returns {Array}
*/
const createRandomPopulation = function(geneticChromosome, populationSize)
{
let population = [];
for(let i = 0; i < populationSize; i++)
{
population.push(geneticChromosome.createRandomChromosome());
}
return population;
};
/**
* Runs the genetic algorithm by going through the processes of
* natural selection, mutation, mating, and immigrations. This
* process will continue until an adequately performing chromosome
* is found or a generation threshold is passed.
*
* @param geneticChromosome Prototypical chromosome: used so algo knows
* what the dna of the population looks like.
* @param costFunction Function which defines how bad a Chromosome is
* @param populationSize Desired population size for population
* @param maxGenerations Cut off level for number of generations to run
* @param desiredCost Sufficient cost to terminate program at
* @param mutationRate Number between [0,1] representing proportion of genes
* to mutate each generation
* @param keepNumber Number of Organisms which survive each generation
* @param newBloodNumber Number of random immigrants to introduce into
* the population each generation.
* @returns {*}
*/
const runGeneticOptimization = function(geneticChromosome, costFunction,
populationSize, maxGenerations,
desiredCost, mutationRate, keepNumber,
newBloodNumber)
{
let population = createRandomPopulation(geneticChromosome, populationSize);
let generation = 0;
let bestCost = Number.MAX_VALUE;
let bestChromosome = geneticChromosome;
do
{
matePopulation(population, populationSize);
newBlood(population, newBloodNumber);
mutatePopulation(population, mutationRate);
let generationResult = naturalSelection(population, keepNumber, costFunction);
if(bestCost > generationResult.bestFit)
{
bestChromosome = generationResult.bestChrom;
bestCost = generationResult.bestFit;
}
population = generationResult.survivors;
generation++;
console.log("Generation " + generation + " Best Cost: " + bestCost);
}while(generation < maxGenerations && bestCost > desiredCost);
return bestChromosome;
};
/**
* Ugly globals used to keep track of population state for the graph.
*/
let genericChromosomeG, costFunctionG,
populationSizeG, maxGenerationsG,
desiredCostG, mutationRateG, keepNumberG,
newBloodNumberG, populationG, generationG,
bestCostG = Number.MAX_VALUE, bestChromosomeG = genericChromosomeG;
const runGeneticOptimizationForGraph = function()
{
let generationResult = naturalSelection(populationG, keepNumberG, costFunctionG);
stats.push([generationG, generationResult.bestFit, generationResult.average]);
if(bestCostG > generationResult.bestFit)
{
bestChromosomeG = generationResult.bestChrom;
bestCostG = generationResult.bestFit;
}
populationG = generationResult.survivors;
generationG++;
console.log("Generation " + generationG + " Best Cost: " + bestCostG);
console.log(generationResult);
matePopulation(populationG, populationSizeG);
newBlood(populationG, newBloodNumberG);
mutatePopulation(populationG, mutationRateG);
createGraph();
};
let stats = [];
const createGraph = function()
{
var dataPoints = [];
console.log(dataPoints);
var data = new google.visualization.DataTable();
data.addColumn('number', 'Gene 1');
data.addColumn('number', 'Gene 2');
for(let i = 0; i < populationG.length; i++)
{
data.addRow([populationG[i].getGenes()[0].getRealValue(),
populationG[i].getGenes()[1].getRealValue()]);
}
var options = {
title: 'Genetic Evolution On Two Genes Generation: ' + generationG,
hAxis: {title: 'Gene 1', minValue: 0, maxValue: 10},
vAxis: {title: 'Gene 2', minValue: 0, maxValue: 10},
};
var chart = new google.visualization.ScatterChart(document.getElementById('chart_div'));
chart.draw(data, options);
//line chart stuff
var line_data = new google.visualization.DataTable();
line_data.addColumn('number', 'Generation');
line_data.addColumn('number', 'Best');
line_data.addColumn('number', 'Average');
line_data.addRows(stats);
console.log(stats);
var lineChartOptions = {
hAxis: {
title: 'Generation'
},
vAxis: {
title: 'Cost'
},
colors: ['#AB0D06', '#007329']
};
var chart = new google.visualization.LineChart(document.getElementById('line_chart'));
chart.draw(line_data, lineChartOptions);
};
let gene1 = new Gene(1,10,10);
let gene2 = new Gene(1,10,0.4);
let geneList = [gene1, gene2];
let exampleOrganism = new Chromosome(geneList);
genericChromosomeG = exampleOrganism;
costFunctionG = basicCostFunction;
populationSizeG = 100;
maxGenerationsG = 30;
desiredCostG = 0.00001;
mutationRateG = 0.3;
keepNumberG = 30;
newBloodNumberG = 10;
generationG = 0;
function verifyForm()
{
if(Number($("#populationSize").val()) <= 1)
{
alert("Population size must be greater than one.");
return false;
}
if(Number($("#mutationRate").val()) > 1 ||
Number($("#mutationRate").val()) < 0)
{
alert("Mutation rate must be between zero and one.");
return false;
}
if(Number($("#survivalSize").val()) < 0)
{
alert("Survival size can't be less than one.");
return false;
}
if(Number($("#newBlood").val()) < 0)
{
alert("New organisms can't be a negative number.");
return false;
}
return true;
}
function resetPopulation()
{
if(verifyForm())
{
stats = [];
autoRunning = false;
$("#runAutoOptimizer").val("Auto Run");
populationSizeG = $("#populationSize").val();
mutationRateG = $("#mutationRate").val();
keepNumberG = $("#survivalSize").val();
newBloodNumberG = $("#newBlood").val();
generationG = 0;
populationG = createRandomPopulation(genericChromosomeG, populationSizeG);
createGraph();
}
}
populationG = createRandomPopulation(genericChromosomeG, populationSizeG);
window.onload = function (){
google.charts.load('current', {packages: ['corechart', 'line']});
google.charts.load('current', {'packages':['corechart']}).then(function()
{
createGraph();
})
};
let autoRunning = false;
function runAutoOptimizer()
{
if(autoRunning === true)
{
runGeneticOptimizationForGraph();
setTimeout(runAutoOptimizer, 1000);
}
}
function startStopAutoRun()
{
autoRunning = !autoRunning;
if(autoRunning)
{
$("#runAutoOptimizer").val("Stop Auto Run");
}
else
{
$("#runAutoOptimizer").val("Resume Auto Run");
}
runAutoOptimizer();
}
//runGeneticOptimization(exampleOrganism, basicCostFunction, 100, 50, 0.01, 0.3, 20, 10);
</script>
</head>
<body>
<div id="chart_div" style="width: 900px; height: 500px;"></div>
<div id="line_chart"></div>
<input class='btn btn-primary' id="runOptimizer" onclick='runGeneticOptimizationForGraph()' type="button" value="Next Generation">
<input class='btn btn-primary' id="runAutoOptimizer" onclick='startStopAutoRun()' type="button" value="Auto Run">
<div class="card">
<div class="card-header">
<h2>Population Variables</h2>
</div>
<form class="card-body">
<div class="row p-2">
<div class="col">
<label for="populationSize">Population Size</label>
<input type="text" class="form-control" value="100" id="populationSize" placeholder="Population Size" required>
</div>
<div class="col">
<label for="populationSize">Survival Size</label>
<input type="text" class="form-control" value="20" id="survivalSize" placeholder="Survival Size" required>
</div>
</div>
<div class="row p-2">
<div class="col">
<label for="populationSize">Mutation Rate</label>
<input type="text" class="form-control" value="0.03" id="mutationRate" placeholder="Mutation Rate" required>
</div>
<div class="col">
<label for="populationSize">New Organisms Per Generation</label>
<input type="text" class="form-control" value="5" id="newBlood" placeholder="New Organisms" required>
</div>
</div>
<br>
<input class='btn btn-primary' id="reset" onclick='resetPopulation()' type="button" value="Reset Population">
</form>
</div>
</body>
</html>