diff --git a/Neuroevolution.js b/Neuroevolution.js
index f1bc43d..22c1123 100644
--- a/Neuroevolution.js
+++ b/Neuroevolution.js
@@ -16,7 +16,7 @@ var Neuroevolution = function (options) {
 		 * @return Logistic function output.
 		 */
 		activation: function (a) {
-			ap = (-a) / 1;
+			var ap = (-a) / 1;
 			return (1 / (1 + Math.exp(ap)))
 		},
 
@@ -85,8 +85,9 @@ var Neuroevolution = function (options) {
 	 */
 	Neuron.prototype.populate = function (nb) {
 		this.weights = [];
+		var rfunc = self.options.randomClamped;
 		for (var i = 0; i < nb; i++) {
-			this.weights.push(self.options.randomClamped());
+			this.weights.push(rfunc());
 		}
 	}
 
@@ -154,13 +155,14 @@ var Neuroevolution = function (options) {
 		index++;
 		for (var i in hiddens) {
 			// Repeat same process as first layer for each hidden layer.
-			var layer = new Layer(index);
-			layer.populate(hiddens[i], previousNeurons);
-			previousNeurons = hiddens[i];
-			this.layers.push(layer);
+			var hidden_layer = new Layer(index);
+			var ihidden = hiddens[i];
+			hidden_layer.populate(ihidden, previousNeurons);
+			previousNeurons = ihidden;
+			this.layers.push(hidden_layer);
 			index++;
 		}
-		var layer = new Layer(index);
+		layer = new Layer(index);
 		layer.populate(output, previousNeurons); // Number of input is equal to
 		// the size of the last hidden
 		// layer.
@@ -175,18 +177,22 @@ var Neuroevolution = function (options) {
 	 * @return Network data.
 	 */
 	Network.prototype.getSave = function () {
+		var dneurons = []; // Number of Neurons per layer.
+		var dweights = []; // Weights of each Neuron's inputs.
 		var datas = {
-			neurons: [], // Number of Neurons per layer.
-			weights: [] // Weights of each Neuron's inputs.
+			neurons: dneurons,
+			weights: dweights
 		};
 
 		for (var i in this.layers) {
-			datas.neurons.push(this.layers[i].neurons.length);
-			for (var j in this.layers[i].neurons) {
-				for (var k in this.layers[i].neurons[j].weights) {
+			var neuron = this.layers[i].neurons;
+			dneurons.push(neuron.length);
+			for (var j in neuron) {
+				var weights = neuron[j].weights;
+				for (var k in weights) {
 					// push all input weights of each Neuron of each Layer into a flat
 					// array.
-					datas.weights.push(this.layers[i].neurons[j].weights[k]);
+					dweights.push(weights[k]);
 				}
 			}
 		}
@@ -204,19 +210,22 @@ var Neuroevolution = function (options) {
 		var index = 0;
 		var indexWeights = 0;
 		this.layers = [];
+		var sweights = save.weights;
 		for (var i in save.neurons) {
 			// Create and populate layers.
 			var layer = new Layer(index);
-			layer.populate(save.neurons[i], previousNeurons);
+			var sneuron = save.neurons[i];
+			layer.populate(sneuron, previousNeurons);
 			for (var j in layer.neurons) {
-				for (var k in layer.neurons[j].weights) {
+				var weights = layer.neurons[j].weights;
+				for (var k in weights) {
 					// Apply neurons weights to each Neuron.
-					layer.neurons[j].weights[k] = save.weights[indexWeights];
+					weights[k] = sweights[indexWeights];
 
 					indexWeights++; // Increment index of flat array.
 				}
 			}
-			previousNeurons = save.neurons[i];
+			previousNeurons = sneuron;
 			index++;
 			this.layers.push(layer);
 		}
@@ -230,35 +239,43 @@ var Neuroevolution = function (options) {
 	 */
 	Network.prototype.compute = function (inputs) {
 		// Set the value of each Neuron in the input layer.
-		for (var i in inputs) {
-			if (this.layers[0] && this.layers[0].neurons[i]) {
-				this.layers[0].neurons[i].value = inputs[i];
+		var layer0 = this.layers[0];
+		if(layer0)
+		{
+			var layer0_neurons = layer0.neurons;
+			for (var i in inputs) {
+				if (layer0_neurons[i]) {
+					layer0_neurons[i].value = inputs[i];
+				}
 			}
 		}
 
 		var prevLayer = this.layers[0]; // Previous layer is input layer.
-		for (var i = 1; i < this.layers.length; i++) {
-			for (var j in this.layers[i].neurons) {
+		var activation = self.options.activation;
+		for (var i = 1, len = this.layers.length; i < len; i++) {
+			var pneurons = prevLayer.neurons;
+			var lneurons = this.layers[i].neurons;
+			for (var j in lneurons) {
 				// For each Neuron in each layer.
+				var weights = lneurons[j].weights;
 				var sum = 0;
-				for (var k in prevLayer.neurons) {
+				for (var k in pneurons) {
 					// Every Neuron in the previous layer is an input to each Neuron in
 					// the next layer.
-					sum += prevLayer.neurons[k].value *
-						this.layers[i].neurons[j].weights[k];
+					sum += pneurons[k].value * weights[k];
 				}
 
 				// Compute the activation of the Neuron.
-				this.layers[i].neurons[j].value = self.options.activation(sum);
+				lneurons[j].value = activation(sum);
 			}
 			prevLayer = this.layers[i];
 		}
 
 		// All outputs of the Network.
 		var out = [];
-		var lastLayer = this.layers[this.layers.length - 1];
-		for (var i in lastLayer.neurons) {
-			out.push(lastLayer.neurons[i].value);
+		var lastLayer_neurons = this.layers[this.layers.length - 1].neurons;
+		for (var i in lastLayer_neurons) {
+			out.push(lastLayer_neurons[i].value);
 		}
 		return out;
 	}
@@ -302,15 +319,17 @@ var Neuroevolution = function (options) {
 	Generation.prototype.addGenome = function (genome) {
 		// Locate position to insert Genome into.
 		// The gnomes should remain sorted.
-		for (var i = 0; i < this.genomes.length; i++) {
+		var scoreSort = self.options.scoreSort;
+		for (var i = 0, len = this.genomes.length; i < len; i++) {
 			// Sort in descending order.
-			if (self.options.scoreSort < 0) {
-				if (genome.score > this.genomes[i].score) {
+			var score = this.genomes[i].score;
+			if (scoreSort < 0) {
+				if (genome.score > score) {
 					break;
 				}
 				// Sort in ascending order.
 			} else {
-				if (genome.score < this.genomes[i].score) {
+				if (genome.score < score) {
 					break;
 				}
 			}
@@ -330,25 +349,27 @@ var Neuroevolution = function (options) {
 	 */
 	Generation.prototype.breed = function (g1, g2, nbChilds) {
 		var datas = [];
+		var mutationRate = self.options.mutationRate;
+		var mutationRange = self.options.mutationRange;
+		var g2_weights = g2.network.weights;
 		for (var nb = 0; nb < nbChilds; nb++) {
 			// Deep clone of genome 1.
 			var data = JSON.parse(JSON.stringify(g1));
-			for (var i in g2.network.weights) {
+			var data_weights = data.network.weights;
+			for (var i in g2_weights) {
 				// Genetic crossover
 				// 0.5 is the crossover factor.
 				// FIXME Really should be a predefined constant.
 				if (Math.random() <= 0.5) {
-					data.network.weights[i] = g2.network.weights[i];
+					data_weights[i] = g2_weights[i];
 				}
 			}
 
 			// Perform mutation on some weights.
 			for (var i in data.network.weights) {
-				if (Math.random() <= self.options.mutationRate) {
-					data.network.weights[i] += Math.random() *
-						self.options.mutationRange *
-						2 -
-						self.options.mutationRange;
+				if (Math.random() <= mutationRate) {
+					data_weights[i] += Math.random() *
+						mutationRange * 2 - mutationRange;
 				}
 			}
 			datas.push(data);
@@ -365,34 +386,38 @@ var Neuroevolution = function (options) {
 	Generation.prototype.generateNextGeneration = function () {
 		var nexts = [];
 
-		for (var i = 0; i < Math.round(self.options.elitism *
-				self.options.population); i++) {
-			if (nexts.length < self.options.population) {
+		var population = self.options.population;
+		for (var i = 0, len= Math.round(self.options.elitism *
+				self.options.population); i < len; i++) {
+			if (nexts.length < population) {
 				// Push a deep copy of ith Genome's Nethwork.
 				nexts.push(JSON.parse(JSON.stringify(this.genomes[i].network)));
 			}
 		}
 
-		for (var i = 0; i < Math.round(self.options.randomBehaviour *
-				self.options.population); i++) {
-			var n = JSON.parse(JSON.stringify(this.genomes[0].network));
+		var funcRC = self.options.randomClamped;
+		var n = JSON.parse(JSON.stringify(this.genomes[0].network));
+		for (var i = 0, len = Math.round(self.options.randomBehaviour *
+				self.options.population); i < len; i++) {
 			for (var k in n.weights) {
-				n.weights[k] = self.options.randomClamped();
+				n.weights[k] = funcRC();
 			}
-			if (nexts.length < self.options.population) {
+			if (nexts.length < population) {
 				nexts.push(n);
 			}
 		}
 
 		var max = 0;
+		var nbChild = self.options.nbChild;
 		while (true) {
+			var genomes_max = this.genomes[max];
 			for (var i = 0; i < max; i++) {
 				// Create the children and push them to the nexts array.
-				var childs = this.breed(this.genomes[i], this.genomes[max],
-					(self.options.nbChild > 0 ? self.options.nbChild : 1));
+				var childs = this.breed(this.genomes[i], genomes_max,
+					(nbChild > 0 ? nbChild : 1));
 				for (var c in childs) {
 					nexts.push(childs[c].network);
-					if (nexts.length >= self.options.population) {
+					if (nexts.length >= population) {
 						// Return once number of children is equal to the
 						// population by generatino value.
 						return nexts;
@@ -417,7 +442,7 @@ var Neuroevolution = function (options) {
 	 */
 	var Generations = function () {
 		this.generations = [];
-		var currentGeneration = new Generation();
+		//var currentGeneration = new Generation();
 	}
 
 	/**
@@ -432,12 +457,14 @@ var Neuroevolution = function (options) {
 		// FIXME input, hiddens, output unused.
 
 		var out = [];
+		var network = self.options.network;
+		var network0 = network[0];
+		var network1 = network[1];
+		var network2 = network[2];
 		for (var i = 0; i < self.options.population; i++) {
 			// Generate the Network and save it.
 			var nn = new Network();
-			nn.perceptronGeneration(self.options.network[0],
-				self.options.network[1],
-				self.options.network[2]);
+			nn.perceptronGeneration(network0, network1, network2);
 			out.push(nn.getSave());
 		}
 
diff --git a/game.js b/game.js
index 0864638..f910563 100644
--- a/game.js
+++ b/game.js
@@ -39,14 +39,15 @@ var loadImages = function(sources, callback){
 	var imgs = {};
 	for(var i in sources){
 		nb++;
-		imgs[i] = new Image();
-		imgs[i].src = sources[i];
-		imgs[i].onload = function(){
+		var img = new Image();
+		img.src = sources[i];
+		img.onload = function(){
 			loaded++;
 			if(loaded == nb){
 				callback(imgs);
 			}
 		}
+		imgs[i] = img;
 	}
 }
 
@@ -84,16 +85,17 @@ Bird.prototype.isDead = function(height, pipes){
 		return true;
 	}
 	for(var i in pipes){
+		var ipipe = pipes[i];
 		if(!(
-			this.x > pipes[i].x + pipes[i].width ||
-			this.x + this.width < pipes[i].x || 
-			this.y > pipes[i].y + pipes[i].height ||
-			this.y + this.height < pipes[i].y
+			this.x > ipipe.x + ipipe.width ||
+			this.x + this.width < ipipe.x || 
+			this.y > ipipe.y + ipipe.height ||
+			this.y + this.height < ipipe.y
 			)){
 			return true;
+		}
 	}
 }
-}
 
 var Pipe = function(json){
 	this.x = 0;
@@ -158,30 +160,33 @@ Game.prototype.update = function(){
 	this.backgroundx += this.backgroundSpeed;
 	var nextHoll = 0;
 	if(this.birds.length > 0){
-		for(var i = 0; i < this.pipes.length; i+=2){
-			if(this.pipes[i].x + this.pipes[i].width > this.birds[0].x){
-				nextHoll = this.pipes[i].height/this.height;
+		var bird0_x = this.birds[0].x;
+		for(var i = 0, len = this.pipes.length; i < len; i+=2){
+			var ipipe = this.pipes[i];
+			if(ipipe.x + ipipe.width > bird0_x){
+				nextHoll = ipipe.height/this.height;
 				break;
 			}
 		}
 	}
 
 	for(var i in this.birds){
-		if(this.birds[i].alive){
+		var ibird = this.birds[i];
+		if(ibird.alive){
 
 			var inputs = [
-			this.birds[i].y / this.height,
+			ibird.y / this.height,
 			nextHoll
 			];
 
 			var res = this.gen[i].compute(inputs);
 			if(res > 0.5){
-				this.birds[i].flap();
+				ibird.flap();
 			}
 
-			this.birds[i].update();
-			if(this.birds[i].isDead(this.height, this.pipes)){
-				this.birds[i].alive = false;
+			ibird.update();
+			if(ibird.isDead(this.height, this.pipes)){
+				ibird.alive = false;
 				this.alives--;
 				//console.log(this.alives);
 				Neuvol.networkScore(this.gen[i], this.score);
@@ -193,8 +198,9 @@ Game.prototype.update = function(){
 	}
 
 	for(var i = 0; i < this.pipes.length; i++){
-		this.pipes[i].update();
-		if(this.pipes[i].isOut()){
+		var ipipe = this.pipes[i];
+		ipipe.update();
+		if(ipipe.isOut()){
 			this.pipes.splice(i, 1);
 			i--;
 		}
@@ -240,26 +246,31 @@ Game.prototype.isItEnd = function(){
 
 Game.prototype.display = function(){
 	this.ctx.clearRect(0, 0, this.width, this.height);
-	for(var i = 0; i < Math.ceil(this.width / images.background.width) + 1; i++){
-		this.ctx.drawImage(images.background, i * images.background.width - Math.floor(this.backgroundx%images.background.width), 0)
+	var imgbg = images.background;
+	var mfb = Math.floor(this.backgroundx%imgbg.width);
+	for(var i = 0, len = Math.ceil(this.width / imgbg.width) + 1; i < len; i++){
+		this.ctx.drawImage(imgbg, i * imgbg.width - mfb, 0);
 	}
 
+	var imgs_ph = images.pipetop.height;
 	for(var i in this.pipes){
+		var ipipe = this.pipes[i];
 		if(i%2 == 0){
-			this.ctx.drawImage(images.pipetop, this.pipes[i].x, this.pipes[i].y + this.pipes[i].height - images.pipetop.height, this.pipes[i].width, images.pipetop.height);
+			this.ctx.drawImage(images.pipetop, ipipe.x, ipipe.y + ipipe.height - imgs_ph, ipipe.width, imgs_ph);
 		}else{
-			this.ctx.drawImage(images.pipebottom, this.pipes[i].x, this.pipes[i].y, this.pipes[i].width, images.pipetop.height);
+			this.ctx.drawImage(images.pipebottom, ipipe.x, ipipe.y, ipipe.width, imgs_ph);
 		}
 	}
 
 	this.ctx.fillStyle = "#FFC600";
 	this.ctx.strokeStyle = "#CE9E00";
 	for(var i in this.birds){
-		if(this.birds[i].alive){
+		var ibird = this.birds[i];
+		if(ibird.alive){
 			this.ctx.save(); 
-			this.ctx.translate(this.birds[i].x + this.birds[i].width/2, this.birds[i].y + this.birds[i].height/2);
-			this.ctx.rotate(Math.PI/2 * this.birds[i].gravity/20);
-			this.ctx.drawImage(images.bird, -this.birds[i].width/2, -this.birds[i].height/2, this.birds[i].width, this.birds[i].height);
+			this.ctx.translate(ibird.x + ibird.width/2, ibird.y + ibird.height/2);
+			this.ctx.rotate(Math.PI/2 * ibird.gravity/20);
+			this.ctx.drawImage(images.bird, -ibird.width/2, -ibird.height/2, ibird.width, ibird.height);
 			this.ctx.restore();
 		}
 	}