diff --git a/GaSchedule.Algorithm/Emosoa.cs b/GaSchedule.Algorithm/Emosoa.cs
deleted file mode 100644
index 18fc64e..0000000
--- a/GaSchedule.Algorithm/Emosoa.cs
+++ /dev/null
@@ -1,199 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using GaSchedule.Model;
-
-/*
- * Dhiman, Gaurav & Singh, Krishna & Slowik, Adam & Chang, Victor & Yildiz, Ali & Kaur, Amandeep & Garg, Meenakshi. (2021).
- * EMoSOA: A New Evolutionary Multi-objective Seagull Optimization Algorithm for Global Optimization.
- * International Journal of Machine Learning and Cybernetics. 12. 10.1007/s13042-020-01189-1.
- * Copyright (c) 2022 Miller Cy Chan
- */
-
-namespace GaSchedule.Algorithm
-{
-	/****************** Evolutionary multi-objective seagull optimization algorithm (EMoSOA) **********************/
-	public class Emosoa<T> : NsgaII<T> where T : Chromosome<T>
-	{
-		private int _currentGeneration = 0, _max_iterations = 5000;
-		private float _gBestScore;
-		private float[] _bestScore;
-		private float[] _gBest = null;
-		private float[][] _current_position = null;
-
-		// Initializes Evolutionary multi-objective seagull optimization algorithm
-		public Emosoa(T prototype, int numberOfCrossoverPoints = 2, int mutationSize = 2, float crossoverProbability = 80, float mutationProbability = 3) : base(prototype, numberOfCrossoverPoints, mutationSize, crossoverProbability, mutationProbability)
-		{
-		}
-
-		static E[][] CreateArray<E>(int rows, int cols)
-		{
-			E[][] array = new E[rows][];
-			for (int i = 0; i < array.GetLength(0); i++)
-				array[i] = new E[cols];
-
-			return array;
-		}
-
-		private void Exploitation(List<T> population)
-		{
-			var tau = 2 * Math.PI;
-
-			var A = 2 - _currentGeneration * (2.0 / _max_iterations);
-			var B = (2 * A * A) * Configuration.Random();
-
-			for (int i = 0; i < population.Count; ++i)
-			{
-				int dim = _current_position[i].Length;
-				for (int j = 0; j < dim; ++j)
-				{
-					var C = A * _current_position[i][j];
-					var M = B * (_gBest[j] - _current_position[i][j]);
-					var D = Math.Abs(C + M);
-					var theta = Configuration.Rand(0d, tau);
-					var r = Math.Exp(theta);
-
-					double x = r * Math.Cos(theta), y = r * Math.Sin(theta), z = r * theta;
-					_current_position[i][j] = (float)(D * x * y * z + _gBest[j]);
-				}
-			}
-		}
-
-		protected override List<T> Replacement(List<T> population)
-		{
-			var populationSize = population.Count;
-			var climax = .9f;
-
-			for (int i = 0; i < populationSize; ++i)
-			{
-				var fitness = population[i].Fitness;
-				if (fitness < _bestScore[i])
-				{
-					population[i].UpdatePositions(_current_position[i]);
-					fitness = population[i].Fitness;
-				}
-
-				if (fitness > _bestScore[i])
-				{
-					_bestScore[i] = fitness;
-					population[i].ExtractPositions(_current_position[i]);
-				}
-
-				if (fitness > _gBestScore)
-				{
-					_gBestScore = fitness;
-					population[i].ExtractPositions(_current_position[i]);
-					_gBest = _current_position[i].ToArray();
-				}
-
-				if (_repeatRatio > climax && _gBestScore > climax) {
-					if (i > (populationSize * _repeatRatio))
-						population[i].UpdatePositions(_current_position[i]);
-				}
-			}
-
-			Exploitation(population);
-			return base.Replacement(population);
-		}
-
-
-		protected override void Initialize(List<T> population)
-		{
-			int size = 0;
-			int numberOfChromosomes = _populationSize;
-			for (int i = 0; i < _populationSize; ++i)
-			{
-				List<float> positions = new();
-
-				// add new search agent to population
-				population.Add(_prototype.MakeNewFromPrototype(positions));
-
-				if (i < 1)
-				{
-					size = positions.Count;
-					_current_position = CreateArray<float>(numberOfChromosomes, size);
-					_gBest = new float[numberOfChromosomes];
-					_bestScore = new float[numberOfChromosomes];
-				}
-
-				_bestScore[i] = population[i].Fitness;
-				for (int j = 0; j < size; ++j)
-					_current_position[i][j] = positions[j];
-			}
-		}
-		
-				// Starts and executes algorithm
-		public override void Run(int maxRepeat = 9999, double minFitness = 0.999)
-		{
-			if (_prototype == null)
-				return;
-
-			var population = new List<T>();
-			Initialize(population);
-
-			int bestNotEnhance = 0;
-			var lastBestFit = 0.0;
-
-			while(_currentGeneration < _max_iterations)
-			{
-				var best = Result;
-				if (_currentGeneration > 0)
-				{
-					var status = string.Format("\rFitness: {0:F6}\t Generation: {1}", best.Fitness, _currentGeneration);
-					Console.Write(status);
-
-					// algorithm has reached criteria?
-					if (best.Fitness > minFitness)
-						break;
-
-					var difference = Math.Abs(best.Fitness - lastBestFit);
-					if (difference <= 0.0000001)
-						++bestNotEnhance;
-					else {
-						lastBestFit = best.Fitness;
-						bestNotEnhance = 0;
-					}
-
-					_repeatRatio = bestNotEnhance * 100.0f / maxRepeat;
-					if (bestNotEnhance > (maxRepeat / 100))
-						Reform();
-
-				}
-
-				/******************* crossover *****************/
-				var offspring = Replacement(population);
-
-				/******************* mutation *****************/
-				foreach (var child in offspring)
-					child.Mutation(_mutationSize, _mutationProbability);
-
-				var totalChromosome = new List<T>(population);
-				totalChromosome.AddRange(offspring);
-
-				/******************* non-dominated sorting *****************/
-				var front = NonDominatedSorting(totalChromosome);
-
-				/******************* selection *****************/
-				population = Selection(front, totalChromosome);
-				_populationSize = population.Count;
-
-				/******************* comparison *****************/
-				if (_currentGeneration == 0)
-					_chromosomes = population.ToArray();
-				else
-				{
-					totalChromosome = new List<T>(population);
-					totalChromosome.AddRange(_chromosomes);
-					var newBestFront = NonDominatedSorting(totalChromosome);
-					_chromosomes = Selection(newBestFront, totalChromosome).ToArray();
-				}
-				++_currentGeneration;
-			}
-		}
-
-		public override string ToString()
-		{
-			return "Evolutionary multi-objective seagull optimization algorithm for global optimization (EMoSOA)";
-		}
-	}
-}