diff --git a/GaSchedule.Algorithm/Hgasso.cs b/GaSchedule.Algorithm/Hgasso.cs
deleted file mode 100644
index 3711919..0000000
--- a/GaSchedule.Algorithm/Hgasso.cs
+++ /dev/null
@@ -1,139 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using GaSchedule.Model;
-
-/*
- * Shehadeh, Hisham & Mustafa, Hossam & Tubishat, Mohammad. (2022).
- * A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions.
- * International Journal of Applied Metaheuristic Computing. 13. 10.4018/IJAMC.292507.
- * Copyright (c) 2022 Miller Cy Chan
- */
-
-namespace GaSchedule.Algorithm
-{
-    /****************** Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) **********************/
-    public class Hgasso<T> : NsgaII<T> where T : Chromosome<T>
-    {
-        private float _climax = .75f;
-        private float _sgBestScore;
-        private bool[] _motility;
-        private float[] _sBestScore;
-        private float[] _sgBest = null;
-        private float[][] _current_position = null;
-        private float[][] _sBest = null;
-        private float[][] _velocity = null;
-
-        // Initializes Hybrid Genetic Algorithm and Sperm Swarm Optimization
-        public Hgasso(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;
-        }
-
-        protected override void Initialize(List<T> population)
-        {
-            int size = 0;
-            int numberOfChromosomes = _populationSize;
-            for (int i = 0; i < _populationSize; ++i)
-            {
-                List<float> positions = new();
-
-                // initialize new population with chromosomes randomly built using prototype
-                population.Add(_prototype.MakeNewFromPrototype(positions));
-
-                if (i < 1)
-                {
-                    size = positions.Count;
-                    _current_position = CreateArray<float>(numberOfChromosomes, size);
-                    _velocity = CreateArray<float>(numberOfChromosomes, size);
-                    _sBest = CreateArray<float>(numberOfChromosomes, size);
-                    _sgBest = new float[numberOfChromosomes];
-                    _sBestScore = new float[numberOfChromosomes];
-                    _motility = new bool[numberOfChromosomes];
-                }
-
-                _sBestScore[i] = population[i].Fitness;
-                for (int j = 0; j < size; ++j)
-                {
-                    _current_position[i][j] = positions[j];
-                    _velocity[i][j] = (float)(Configuration.Rand(-.6464f, .7157f) / 3.0);
-                }
-            }
-        }
-
-        private void UpdateVelocities(List<T> population)
-        {
-            for (int i = 0; i < population.Count; ++i)
-            {
-                if (!_motility[i])
-                    continue;
-
-                int dim = _velocity[i].Length;
-                for (int j = 0; j < dim; ++j)
-                {
-                    _velocity[i][j] = (float)(Configuration.Random() * Math.Log10(Configuration.Rand(7.0f, 14.0f)) * _velocity[i][j]
-                    + Math.Log10(Configuration.Rand(7.0f, 14.0f)) * Math.Log10(Configuration.Rand(35.5f, 38.5f)) * (_sBest[i][j] - _current_position[i][j])
-                    + Math.Log10(Configuration.Rand(7.0f, 14.0f)) * Math.Log10(Configuration.Rand(35.5f, 38.5f)) * (_sgBest[j] - _current_position[i][j]));
-
-                    _current_position[i][j] += _velocity[i][j];
-                }
-            }
-        }
-
-        protected override List<T> Replacement(List<T> population)
-        {
-            var populationSize = population.Count;
-            var decline = 1 - _climax;
-
-            for (int i = 0; i < populationSize; ++i)
-            {
-                var fitness = population[i].Fitness;
-                if (fitness < _sBestScore[i])
-                {
-                    population[i].UpdatePositions(_current_position[i]);
-                    fitness = population[i].Fitness;
-                    _motility[i] = true;
-                }
-
-                if (fitness > _sBestScore[i])
-                {
-                    _sBestScore[i] = fitness;
-                    population[i].ExtractPositions(_current_position[i]);
-                    _sBest[i] = _current_position[i].ToArray();                    
-                }
-
-                if (fitness > _sgBestScore)
-                {
-                    _sgBestScore = fitness;
-                    population[i].ExtractPositions(_current_position[i]);
-                    _sgBest = _current_position[i].ToArray();
-                }
-
-                if (_repeatRatio > _sBestScore[i])
-                    _sBestScore[i] -= _repeatRatio * decline;
-                if (_repeatRatio > _climax && _sgBestScore > _climax) {
-                    if (i > (populationSize * _sgBestScore)) {
-                        population[i].UpdatePositions(_current_position[i]);
-                        _motility[i] = true;
-                    }					
-                }
-            }
-
-            UpdateVelocities(population);
-            return base.Replacement(population);
-        }
-
-        public override string ToString()
-        {
-            return "Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO)";
-        }
-    }
-}