diff --git a/build/neuralnetwork/core/Genome.php b/build/neuralnetwork/core/Genome.php
index c708762..bd9318c 100644
--- a/build/neuralnetwork/core/Genome.php
+++ b/build/neuralnetwork/core/Genome.php
@@ -227,7 +227,7 @@
 		*/
 		public function mutation($threshold=1){
 			/* (1) Checks @threshold argument */
-			if( floatval($threshold) !== $threshold || $threshold < 0 || $threshold > 1 )
+			if( !is_numeric($threshold) || $threshold < 0 || $threshold > 1 )
 				throw new \Exception('Invalid threshold for Genome mutation.');
 
 			/* (2) Calculates how many neurons/synapses to mutate */
diff --git a/build/neuralnetwork/core/NeuralNetworkCore.php b/build/neuralnetwork/core/NeuralNetworkCore.php
index 65ab56e..47132be 100644
--- a/build/neuralnetwork/core/NeuralNetworkCore.php
+++ b/build/neuralnetwork/core/NeuralNetworkCore.php
@@ -25,11 +25,11 @@
 		/************************************************
 		****             LOCAL ATTRIBUTES            ****
 		************************************************/
-		public  $maxFit; // Maximum fitness of the previous generation
-		public  $minFit; // Minimum fitness of the current generation
-		public  $gnr;    // Current generation index
-		public  $gnm;    // Current genome index
-		private $genome; // Current genome instance
+		private $maxFit;    // Maximum fitness of the previous generation
+		private $fitnesses; // The fitnesses of the current generation's genomes
+		public  $gnr;       // Current generation index
+		public  $gnm;       // Current genome index
+		private $genome;    // Current genome instance
 
 
 
@@ -83,9 +83,6 @@
 				], 'gn' => [
 					'filename' => $absolute_path.'.gn', // will contain genomes of the generation
 					'exists'   => is_file($absolute_path.'.gn')
-				], 'ft' => [
-					'filename' => $absolute_path.'.ft', // will contain genomes' fitness
-					'exists'   => is_file($absolute_path.'.ft')
 				], 'ln' => [
 					'filename' => $absolute_path.'.ln', // will contain learnt best genomes
 					'exists'   => is_file($absolute_path.'.ln')
@@ -370,10 +367,10 @@
 			/* (1) Initializes data & storage */
 			$this->gnr    = 0;
 			$this->gnm    = 0;
-			$this->minFit = null;
 			$this->maxFit = null;
 			FileManager::write($this->storage['gn']['filename'], '');
-			FileManager::write($this->storage['ft']['filename'], '');
+			// FileManager::write($this->storage['ft']['filename'], '');
+			$this->fitnesses = [];
 
 			/* (2) Stores random genomes to storage */
 			for( $g = 0 ; $g < $this->maxGnm ; $g++ ){
@@ -399,10 +396,10 @@
 			/* (1) Initializes data & storage */
 			$this->gnr    = 0;
 			$this->gnm    = 0;
-			$this->minFit = null;
 			$this->maxFit = null;
 			FileManager::write($this->storage['gn']['filename'], '');
-			FileManager::write($this->storage['ft']['filename'], '');
+			// FileManager::write($this->storage['ft']['filename'], '');
+			$this->fitnesses = [];
 
 			/* (2.1) Fetch learnt best genomes */
 			$loadedGenomes = [
@@ -481,7 +478,11 @@
 				throw new \Exception('The learning routine is closed.');
 
 			/* (1) Stores fitness */
-			FileManager::append($this->storage['ft']['filename'], strval($this->genome->getFitness()) );
+			$fit = $this->genome->getFitness();
+
+			if( !$this->antReg || is_null($this->maxFit) || $fit > $this->maxFit )
+				$this->fitnesses[$this->gnm] = $fit;
+			// FileManager::append($this->storage['ft']['filename'], strval($this->genome->getFitness()) );
 
 
 			/* (1) Iterates if possible
@@ -499,12 +500,12 @@
 				$this->gnm = 0;
 
 				/* (2) Fetch the whole generation fitness values */
-				$ftRead = FileManager::read($this->storage['ft']['filename']);
-				$fitnesses = explode("\n", trim($ftRead) );
+				// $ftRead = FileManager::read($this->storage['ft']['filename']);
+				// $this->fitnesses = explode("\n", trim($ftRead) );
 
 
 				/* (3) Checks if fitnessEnd is reached */
-				if( min($fitnesses) == $this->fitEnd ){
+				if( count($this->fitnesses) > 0 && max($this->fitnesses) == $this->fitEnd ){
 
 					/* (1) Get the 2 best genomes */
 					$best = FileManager::readline($this->storage['gn']['filename'], 0);
@@ -522,13 +523,11 @@
 
 
 				/* (4) Checks if theres a fitness maximum evolution */
-				$fitnessEvolution = !$this->antReg || is_null($this->maxFit) && is_null($this->minFit) || max($fitnesses) > $this->maxFit;
+				// Extract @mother & @father indexes //
+				$iBest = $this->bestFitnesses($this->fitnesses);
 
 				/* (4.1) If evolution -> choose best + cross-over ... */
-				if( $fitnessEvolution ){
-
-					// {1} Extract @mother & @father indexes //
-					$iBest = $this->bestFitnesses($fitnesses);
+				if( !is_null($iBest[0]) ){
 
 					// {2} Extract best 2 genomes //
 					$sFather = FileManager::readline($this->storage['gn']['filename'], $iBest[0]);
@@ -541,8 +540,7 @@
 					$mother = new Genome(2, 2, 2, 2);
 						$mother->unserialize($sMother);
 
-					$this->maxFit = max($fitnesses);
-					$this->minFit = min($fitnesses);
+					$this->maxFit =  max($this->fitnesses);
 
 					$this->storeLearntBest();
 
@@ -566,7 +564,7 @@
 
 				/* (7) Create new generation */
 				FileManager::write($this->storage['gn']['filename'], '');
-				FileManager::write($this->storage['ft']['filename'], '');
+				// FileManager::write($this->storage['ft']['filename'], '');
 
 				for( $g = 0 ; $g < $this->maxGnm ; $g++ ){
 
@@ -587,6 +585,8 @@
 
 				}
 
+				$this->fitnesses = [];
+
 			/* (3) If end of process
 			---------------------------------------------------------*/
 			}else{
@@ -611,7 +611,6 @@
 			/* (2) Stores data to learnt data */
 			FileManager::write($this->storage['ln']['filename'], $best);
 		}
-		// TODO: Manage @mutThr decreasing to be more precise
 
 		/************************************************
 		****                 Utility                 ****
@@ -646,8 +645,8 @@
 				$c++;
 			}
 
-			/* (3) Anti-regression, if @mother < @father only use @father*/
-			if( $fitnesses[$iMother] < $fitnesses[$iFather] )
+			/* (3) If mother not found copy father */
+			if( is_null($iMother)  )
 				$iMother = $iFather;
 
 			return [ $iFather, $iMother ];
diff --git a/config/neural-network.json b/config/neural-network.json
index af81521..edea878 100644
--- a/config/neural-network.json
+++ b/config/neural-network.json
@@ -2,7 +2,7 @@
 	"storage_parent": "/build/neuralnetwork/storage",
 
 	"default": {
-		"mutation_threshold": 0.5,
+		"mutation_threshold": 1,
 		"fitness_end":        1,
 		"storage":            "_buffer",
 		"hidden_layers":      2,
diff --git a/public/main.php b/public/main.php
index c71d7b7..53ff6a6 100644
--- a/public/main.php
+++ b/public/main.php
@@ -12,7 +12,7 @@
 
 
 
-	$train = true;
+	$train = $argc > 1 && $argv[1] == 'train';
 	$guess = !$train;
 
 	if( $train && 'learning_process' ){
@@ -34,13 +34,13 @@
 		=========================================================*/
 		}catch(\Exception $e){
 
-			$nn = NeuralNetwork::create(50, 500);
+			$nn = NeuralNetwork::create(50, 100);
 
-			$nn->setHiddenLayersCount(3);       // 3 Hidden layers
-			$nn->setHiddenLayerNeuronsCount(4); // Composed with 3 neurons each
+			$nn->setHiddenLayersCount(5);       // 3 Hidden layers
+			$nn->setHiddenLayerNeuronsCount(3); // Composed with 3 neurons each
 			$nn->setInputLayerCount(3);         // 3 inputs
 			$nn->setOutputLayerCount(1);        // 1 output
-			$nn->setMutationThreshold(0.3);     // mutation 30% each generation
+			$nn->setMutationThreshold(0.5);     // mutation 30% each generation
 			$nn->setFitnessEnd(-1.5);           // Algorithm is done when fitness reaches 0
 			$nn->setAntiRegression(true);       // That repeats a generation while its fitness is lower than the previous one
 
@@ -71,12 +71,11 @@
 
 		/* [2] Initializing learning routine
 		=========================================================*/
-		$defaultMT = 0.3;
 		$fitness = 0;
 		$max_fit = 0;
 		$nn->loadLearningRoutine(function($input, $output){
 			global $fitness;
-			$fitness -= abs($output[0] - behaviourtest2($input)[0]);
+			$fitness -= abs(round($output[0]) - behaviourtest2($input)[0]);
 		});
 		echo "$part. Learning routine initialized.\n"; $part++;
 
@@ -94,7 +93,6 @@
 			$last_gnr = $nn->gnr;
 
 			$max_fit = -1e9;
-			$min_fit = 100;
 
 			/* (2) For each genome */
 			while( true ){
@@ -102,19 +100,18 @@
 
 				/* (2.1) Get current genome */
 				$g = $nn->getGenome();
-				echo "\r[x] gnm ".($nn->gnm+1)."/500 on gnr ".($nn->gnr+1)."/50 - x".($gen_repeat+1)." - fit[$min_fit;$max_fit]                 ";
+				echo "\r[x] gnm ".($nn->gnm+1)."/100 on gnr ".($nn->gnr+1)."/50 - x".($gen_repeat+1)." - fit[$max_fit]                 ";
 
 				/* (2.2) Train genome with random samples */
-				for( $r = 0 ; $r < 100 ; $r++ )
-					$g->train([rand(0,10), rand(0,10), rand(0,10)]);
+				for( $r = 0 ; $r < 500 ; $r++ )
+					$g->train([rand(0,100), rand(0,100), rand(0,100)]);
 
 				/* (2.3) Set fitness & go to next genome */
 				if( $fitness > $max_fit ) $max_fit = $fitness;
-				if( $fitness < $min_fit ) $min_fit = $fitness;
 
 				$g->setFitness($fitness);
 
-				if( $nn->gnm >= 500-1 )
+				if( $nn->gnm >= 100-1 )
 					break;
 
 				$nn->nextGenome();