// public float probPermutation;
// public float probEncaptilation;
// public bool bEditing;
// public bool bDecimation;
/// public float probReproduction;
public GPParameters()
{
//initial value of GA
algorithmType = Algorithm.GP;
chromosomeKind = GAChromosome.Continue;
einitializationMethod = GPInitializationMethod.FullInitialization;
eselectionMethod = GPSelectionMethod.Rankselection;
GPFitness = null;//new RMSEFitness();
SelParam1 = 3;
SelParam2 = 0;
elitism = 1;
maxInitLevel = 5;
probCrossover = 0.99F;
probMutation = 0.10F;
probReproduction = 0.20F;
popSize = 1000;
broodSize = 1;
isProtectedOperationEnabled = true;
rConstFrom = -1;
rConstTo = 1;
rConstNum = 5;
bParalelGP = false;
}
/// <summary>
///
/// </summary>
/// <param name="chromose"></param>
private void Initializer(GAChromosome chromose)
{
bool [] PointSel = new bool[Points.Count];
bool bStop = false;
do
{
int iSel = RndObj.Next(0, Points.Count);
if (!PointSel[iSel])
{
PointSel[iSel] = true;
chromose.AddGene(new GAGene(iSel.ToString()));
}
for (int i = 0; i < Points.Count; i++)
{
if (!PointSel[i])
{
break;
}
if (i == Points.Count - 1)
{
bStop = true;
}
}
}while(!bStop);
}
private void button1_Click(object sender, EventArgs e)
{
//初始化
int p = int.Parse(this.textBox2.Text);
int Generations = int.Parse(this.textBox4.Text);//迭代次数
if (p <= Parameter.m)
{
Parameter.p = p;
GA GAAlgo = new GA();
System.Collections.ArrayList bestFitness = new System.Collections.ArrayList();
GAAlgo.MutationRate = double.Parse(this.textBox5.Text);
GAAlgo.CrossRate = double.Parse(this.textBox6.Text);
if (comboBox1.Text == (string)comboBox1.Items[1])
{
GAAlgo.Selection = GA.SelectionType.Tournment;
}
if (comboBox2.Text == (string)comboBox2.Items[1])
{
GAAlgo.Crosstype = 2;
}
//寻优
GAAlgo.Initialize();//产生第一代群体
progressBar1.Value = 0;
GAChromosome GAChr = new GAChromosome();
bestFitness.Clear();
while (GAAlgo.GenerationNum < Generations)//产生下一代群体
{
GAAlgo.CreateNextGeneration();
GAChr = GAAlgo.GetBestChromosome();
bestFitness.Add(GAChr.Fitness);
}
progressBar1.Value = 100;
//记录结果
Parameter.x = GAChr.ToArray();
Parameter.opti = 100 / GAChr.Fitness;
//显示结果
label10.Text = (100 / GAChr.Fitness).ToString();
//图像分析
GAChartForm.chartControl1.Series.Clear();
if (checkBox1.Checked)
{
ChartSeries fitnessSeries = new ChartSeries("bestFitness");
fitnessSeries.SeriesIndexedModelImpl = new StringIndexedModel(fitnessSeries, (double[])bestFitness.ToArray(typeof(double)));
GAChartForm.chartControl1.Series.Add(fitnessSeries);
}
if (checkBox2.Checked)
{
ChartSeries total_fitnessSeries = new ChartSeries("totalFitness");
total_fitnessSeries.SeriesIndexedModelImpl = new StringIndexedModel(total_fitnessSeries, (double[])GAAlgo.TotalFitness.ToArray(typeof(double)));
GAChartForm.chartControl1.Series.Add(total_fitnessSeries);
}
for (int i = 0; i < GAChartForm.chartControl1.Series.Count; i++)
{
GAChartForm.chartControl1.Series[i].Type = ChartSeriesType.Line;
}
}
}
private void Clear_Click(object sender, System.EventArgs e)
{
m_chro = null;
this.Refresh();
Points.Clear();
//新加的部分
points.Clear();
temps.Clear();
}
/// <summary>
/// Steps
/// 1- locate any gene locus randomaly in dad
/// 2- locate this gene index in mum
/// 3- Start Copying new genes from dad direction right
/// 4- Start Copying new genes from mum direction left
/// 5- When exit loop copy the rest of genes
/// </summary>
/// <param name="Dad"></param>
/// <param name="Mum"></param>
/// <param name="child1"></param>
/// <param name="child2"></param>
private void CrossOver
(
GAChromosome Dad
, GAChromosome Mum
, ref GAChromosome child1
, ref GAChromosome child2
)
{
GreedyCrossOver(Dad, Mum, ref child1);
GreedyCrossOver(Mum, Dad, ref child2);
}
private void btn_Run_Click(object sender, System.EventArgs e)
{
try
{
if (Points.Count < 2)
{
MessageBox.Show("No Enough cities");
return;
}
this.m_chro = null;
Refresh();
//新加的部分
points.Clear();
temps.Clear();
DrawPoints();
GALib.Initializer newItializer = new GALib.Initializer(this.Initializer);
GALib.Mutate mutater = new GALib.Mutate(this.ChromoseCompraror);
GALib.Fitness fitmethod = new GALib.Fitness(this.Fitness);
GALib.CrossOver CrossMethod = new GALib.CrossOver(this.CrossOver);
GALib.GA GAAlgo = new GA(newItializer, fitmethod, mutater, CrossMethod);
GAAlgo.Generations = long.Parse(this.num_Gnr.Value.ToString());
GAAlgo.PopulationSize = ushort.Parse(this.num_PopSiz.Value.ToString());
GAAlgo.Mutation = double.Parse(this.num_Mutation.Value.ToString());
GAAlgo.CrossOver = double.Parse(this.numCO.Value.ToString());
if (Tool_MainBar.Buttons[1].Pushed)
{
GAAlgo.EnableLogging = true;
GAAlgo.LogFilePath = this.openFileDialog.FileName;
}
GAAlgo.Initialize();
while (!GAAlgo.IsDone())
{
GAAlgo.CreateNextGeneration();
}
m_chro = GAAlgo.GetBestChromosome();
DrawCitiesPath();
}
catch (System.FormatException exp)
{
MessageBox.Show("Please check your Input Parameters " + exp);
}
}
/// <summary>
///
/// </summary>
/// <param name="chromose"></param>
private void Fitness(GAChromosome chromosome)
{
double fitness = 0;
int CityIndex1 = 0;
int CityIndex2 = 1;
while (CityIndex2 < chromosome.GeneLength)
{
string strGene1 = ((GAGene)chromosome[CityIndex1]).Value;
int Pointindex1 = int.Parse(strGene1);
string strGene2 = ((GAGene)chromosome[CityIndex2]).Value;
int PointIndex2 = int.Parse(strGene2);
fitness += Distance((Point)Points[Pointindex1], (Point)Points[PointIndex2]);
CityIndex1++;
CityIndex2++;
}
//Fitness increases when distance decreases
chromosome.Fitness = 1000 / fitness;
}
/// <summary>
/// Based On 2opt Algo
/// </summary>
/// <param name="chromose"></param>
private void ChromoseCompraror(GAChromosome chromosome)
{
double [,] NighborMatrix = new double[Points.Count, Points.Count];
for (int i = 0; i < Points.Count; i++)
{
NighborMatrix[i, i] = -1; //distance diagonal
}
//start filling the Connection Matrix with cities and distances between each other
for (int i = 0; i < Points.Count - 1; i++)
{
int City1Index = int.Parse(((GAGene)chromosome[i]).Value);
for (int j = i + 1; j < Points.Count; j++)
{
int City2Index = int.Parse(((GAGene)chromosome[j]).Value);
double distance = Distance((Point)Points[City1Index], (Point)Points[City2Index]);
NighborMatrix[City1Index, City2Index] = distance;
NighborMatrix[City2Index, City1Index] = distance;
}
}
GAChromosome newChromosome = new GAChromosome();
//Start By Random Selection
int iCurrentSel = RndObj.Next(0, Points.Count);
GAGene Gene = (GAGene)chromosome[iCurrentSel];
newChromosome.AddGene(new GAGene(Gene.Value));
//left cities for visiting
int iLeftPoints = Points.Count - 1;
int iCurrentCitySel = int.Parse(Gene.Value);
do
{
int iNearstNeighbor = GetNearstNeighbor(NighborMatrix, iCurrentCitySel);
newChromosome.AddGene(new GAGene(iNearstNeighbor.ToString()));
iLeftPoints--;
iCurrentCitySel = iNearstNeighbor;
}while(iLeftPoints > 0);
chromosome.CopyChromosome(newChromosome);
}
/// <summary>
/// Mutation is done By Swapping elements from Chromosome
/// </summary>
/// <param name="chromose"></param>
private void Mutator(GAChromosome chromose)
{
System.Random rnd = new Random();
int ChromoLen = chromose.Capacity;
int iSelection1 = rnd.Next(0, ChromoLen);
int iSelection2 = iSelection1;
while (iSelection2 == iSelection1)
{
iSelection2 = rnd.Next(0, ChromoLen);
}
GAGene Gene1 = (GAGene)chromose[iSelection1];
GAGene Gene2 = (GAGene)chromose[iSelection2];
chromose.RemoveAt(iSelection1);
chromose.Insert(iSelection1, Gene2);
chromose.RemoveAt(iSelection2);
chromose.Insert(iSelection1, Gene1);
}
/// <summary>
/// Mutation is done By Swapping elements from Chromosome
/// </summary>
/// <param name="chromose"></param>
private void Mutator(GAChromosome chromose)
{
System.Random rnd = new Random();
int ChromoLen = chromose.Capacity;
int iSelection1 = rnd.Next(0, ChromoLen);
int iSelection2 = iSelection1;
while(iSelection2 == iSelection1)
iSelection2 = rnd.Next(0, ChromoLen);
GAGene Gene1 = (GAGene)chromose[iSelection1];
GAGene Gene2 = (GAGene)chromose[iSelection2];
chromose.RemoveAt(iSelection1);
chromose.Insert(iSelection1,Gene2);
chromose.RemoveAt(iSelection2);
chromose.Insert(iSelection1,Gene1);
}
/// <summary>
///
/// </summary>
/// <param name="chromose"></param>
private void Initializer(GAChromosome chromose)
{
bool []PointSel = new bool[Points.Count];
bool bStop = false;
do
{
int iSel = RndObj.Next(0,Points.Count);
if (!PointSel[iSel])
{
PointSel[iSel] = true;
chromose.AddGene(new GAGene(iSel.ToString()));
}
for(int i=0; i < Points.Count ;i++)
{
if (!PointSel[i])
break;
if (i == Points.Count -1)
bStop = true;
}
}while(!bStop);
}
/// <summary>
///
/// </summary>
/// <param name="chromose"></param>
private void Fitness(GAChromosome chromosome)
{
double fitness = 0;
int CityIndex1 = 0;
int CityIndex2 = 1;
while (CityIndex2 < chromosome.GeneLength)
{
string strGene1 = ((GAGene)chromosome[CityIndex1]).Value ;
int Pointindex1 = int.Parse(strGene1);
string strGene2 = ((GAGene)chromosome[CityIndex2]).Value ;
int PointIndex2 = int.Parse(strGene2);
fitness += Distance((Point)Points[Pointindex1],(Point)Points[PointIndex2]);
CityIndex1++;
CityIndex2++;
}
//Fitness increases when distance decreases
chromosome.Fitness = 1000/fitness;
}
/// <summary>
/// Steps
/// 1- locate any gene locus randomaly in dad
/// 2- locate this gene index in mum
/// 3- Start Copying new genes from dad direction right
/// 4- Start Copying new genes from mum direction left
/// 5- When exit loop copy the rest of genes
/// </summary>
/// <param name="Dad"></param>
/// <param name="Mum"></param>
/// <param name="child1"></param>
/// <param name="child2"></param>
private void CrossOver(
GAChromosome Dad
, GAChromosome Mum
, ref GAChromosome child1
, ref GAChromosome child2
)
{
GreedyCrossOver(Dad, Mum, ref child1);
GreedyCrossOver(Mum, Dad, ref child2);
}
private void Clear_Click(object sender, System.EventArgs e)
{
m_chro = null ;
this.Refresh();
Points.Clear();
//�¼ӵIJ���
points.Clear();
temps.Clear();
}
/// <summary>
/// Based On 2opt Algo
/// </summary>
/// <param name="chromose"></param>
private void ChromoseCompraror(GAChromosome chromosome)
{
double [,] NighborMatrix = new double[Points.Count , Points.Count];
for (int i = 0; i < Points.Count ; i++)
NighborMatrix[i,i] = -1; //distance diagonal
//start filling the Connection Matrix with cities and distances between each other
for (int i = 0 ; i < Points.Count - 1 ; i++)
{
int City1Index = int.Parse(((GAGene)chromosome[i]).Value);
for (int j = i + 1; j < Points.Count ; j++)
{
int City2Index = int.Parse(((GAGene)chromosome[j]).Value);
double distance = Distance((Point)Points[City1Index],(Point)Points[City2Index]);
NighborMatrix[City1Index, City2Index] = distance ;
NighborMatrix[City2Index, City1Index] = distance ;
}
}
GAChromosome newChromosome = new GAChromosome();
//Start By Random Selection
int iCurrentSel = RndObj.Next(0, Points.Count);
GAGene Gene = (GAGene)chromosome[iCurrentSel];
newChromosome.AddGene(new GAGene(Gene.Value));
//left cities for visiting
int iLeftPoints = Points.Count - 1;
int iCurrentCitySel = int.Parse(Gene.Value);
do
{
int iNearstNeighbor = GetNearstNeighbor(NighborMatrix, iCurrentCitySel);
newChromosome.AddGene(new GAGene(iNearstNeighbor.ToString()));
iLeftPoints--;
iCurrentCitySel = iNearstNeighbor;
}while(iLeftPoints > 0);
chromosome.CopyChromosome(newChromosome);
}
private void btn_Run_Click(object sender, System.EventArgs e)
{
try
{
if (Points.Count < 2)
{
MessageBox.Show("No Enough cities");
return;
}
this.m_chro = null;
Refresh();
//�¼ӵIJ���
points.Clear();
temps.Clear();
DrawPoints();
GALib.Initializer newItializer = new GALib.Initializer(this.Initializer);
GALib.Mutate mutater = new GALib.Mutate(this. ChromoseCompraror);
GALib.Fitness fitmethod = new GALib.Fitness(this.Fitness);
GALib.CrossOver CrossMethod = new GALib.CrossOver(this.CrossOver);
GALib.GA GAAlgo = new GA(newItializer,fitmethod,mutater,CrossMethod);
GAAlgo.Generations = long.Parse(this.num_Gnr.Value.ToString());
GAAlgo.PopulationSize = ushort.Parse(this.num_PopSiz.Value.ToString());
GAAlgo.Mutation = double.Parse(this.num_Mutation.Value.ToString());
GAAlgo.CrossOver = double.Parse(this.numCO.Value.ToString());
if(Tool_MainBar.Buttons[1].Pushed)
{
GAAlgo.EnableLogging = true;
GAAlgo.LogFilePath = this.openFileDialog.FileName;
}
GAAlgo.Initialize();
while (!GAAlgo.IsDone())
GAAlgo.CreateNextGeneration();
m_chro = GAAlgo.GetBestChromosome();
DrawCitiesPath();
}
catch(System.FormatException exp)
{
MessageBox.Show("Please check your Input Parameters "+exp);
}
}
/// <summary>
/// //
/// </summary>
/// <param name="Dad"></param>
/// <param name="Mum"></param>
/// <param name="child1"></param>
private void GreedyCrossOver
(
GAChromosome Dad
, GAChromosome Mum
, ref GAChromosome child
)
{
int length = Dad.GeneLength;
int MumIndex = -1;
int DadIndex = RndObj.Next(0, length);
GAGene DadGene = (GAGene)Dad[DadIndex];
MumIndex = Mum.HasThisGene(DadGene);
if (MumIndex < 0)
{
throw new Exception("Gene not found in mum");
}
child.Add(new GAGene(DadGene.Value));
bool bDadAdded = true;
bool bMumAdded = true;
do
{
//As long as I can add from dad
GAGene obMumGene = null;
GAGene obDadGene = null;
if (bDadAdded)
{
if (DadIndex > 0)
{
DadIndex = DadIndex - 1;
}
else
{
DadIndex = length - 1;
}
obDadGene = (GAGene)Dad[DadIndex];
}
else
{
bDadAdded = false;
}
//As long as I can add from mum
if (bMumAdded)
{
if (MumIndex < length - 1)
{
MumIndex = MumIndex + 1;
}
else
{
MumIndex = 0;
}
obMumGene = (GAGene)Mum[MumIndex];
}
else
{
bMumAdded = false;
}
if (bDadAdded && child.HasThisGene(obDadGene) < 0)
{
//Add to head Dad gene
child.Insert(0, obDadGene);
}
else
{
bDadAdded = false;
}
if (bMumAdded && child.HasThisGene(obMumGene) < 0)
{
//Add to Tail Mum gene
child.AddGene(obMumGene);
}
else
{
bMumAdded = false;
}
}while(bDadAdded || bMumAdded);
// Add rest of genes by Random Selection
while (child.GeneLength < length)
{
bool bDone = false;
do
{
int iRandom = this.RndObj.Next(0, length);
if (child.HasThisGene(new GAGene(iRandom.ToString())) < 0)
{
child.Add(new GAGene(iRandom.ToString()));
bDone = true;
}
}while(!bDone);
}
}
/// <summary>
/// //
/// </summary>
/// <param name="Dad"></param>
/// <param name="Mum"></param>
/// <param name="child1"></param>
private void GreedyCrossOver(
GAChromosome Dad
, GAChromosome Mum
, ref GAChromosome child
)
{
int length = Dad.GeneLength;
int MumIndex = -1;
int DadIndex = RndObj.Next(0,length);
GAGene DadGene = (GAGene)Dad[DadIndex];
MumIndex = Mum.HasThisGene(DadGene);
if (MumIndex < 0 )
throw new Exception("Gene not found in mum");
child.Add(new GAGene(DadGene.Value));
bool bDadAdded = true;
bool bMumAdded = true;
do
{
//As long as I can add from dad
GAGene obMumGene = null;
GAGene obDadGene = null;
if (bDadAdded )
{
if(DadIndex > 0)
DadIndex = DadIndex - 1 ;
else
DadIndex = length - 1 ;
obDadGene = (GAGene)Dad[DadIndex];
}
else
{
bDadAdded = false;
}
//As long as I can add from mum
if (bMumAdded)
{
if(MumIndex < length-1)
MumIndex = MumIndex + 1 ;
else
MumIndex = 0 ;
obMumGene = (GAGene)Mum[MumIndex];
}
else
{
bMumAdded = false;
}
if(bDadAdded && child.HasThisGene(obDadGene)< 0)
{
//Add to head Dad gene
child.Insert(0,obDadGene);
}
else
bDadAdded = false;
if (bMumAdded && child.HasThisGene(obMumGene) < 0)
{
//Add to Tail Mum gene
child.AddGene(obMumGene);
}
else
bMumAdded = false;
}while(bDadAdded || bMumAdded) ;
// Add rest of genes by Random Selection
while (child.GeneLength < length)
{
bool bDone = false;
do
{
int iRandom = this.RndObj.Next(0, length);
if (child.HasThisGene(new GAGene(iRandom.ToString()))< 0)
{
child.Add(new GAGene(iRandom.ToString()));
bDone = true;
}
}while(!bDone);
}
}