private static int swapChance = 9900; // 2 / 10000
#endregion Fields
#region Methods
private static double CalculateFitness(Chromosome c)
{
return CalculateFitness(c, false);
}
// Lower = better.
private static double CalculateFitness(Chromosome c, bool explain)
{
double targetValue = 0;
c.FitnessBonus = 1;
c.Fitness = 0;
// I forget why I comment out trying to get other variables.
foreach (KeyValuePair<string, Dictionary<string, double>> kvp in dict)
{
double r, s, u, v, w, x, y, z;
// For now, gotta change these when changing problem sets!
//kvp.Value.TryGetValue("R", out r);
//kvp.Value.TryGetValue("S", out s);
//kvp.Value.TryGetValue("U", out u);
//kvp.Value.TryGetValue("V", out v);
//kvp.Value.TryGetValue("W", out w);
kvp.Value.TryGetValue("X", out x);
kvp.Value.TryGetValue("Y", out y);
kvp.Value.TryGetValue("Z", out z);
//c.R = r;
//c.S = s;
//targetValue = u;
//c.U = u; // Er, we don't want this in the equation. This is our target!
//c.V = v;
//c.W = w;
c.X = x; // For simple equations, X is our target.
//c.Y = y; // For simple equations, Y is our target.
targetValue = y;
//targetValue = z;
//c.Z = z;
c.ForceCalculate();
if (explain) Console.WriteLine(targetValue);
if (explain) Console.WriteLine(c.X);
if (explain) Console.WriteLine(c.Y);
//Console.WriteLine(c.Result);
//Console.WriteLine(c.Result / targetValue);
//Console.WriteLine(c.Genes.Count);
if (explain) Console.WriteLine(c.IsValid);
if (!c.IsValid)
{
c.Fitness += maxFitness;
break;
}
double tFitness = Math.Max(Math.Abs(c.Result - targetValue), minFitness);
if (explain) Console.WriteLine("Base: " + tFitness);
// Penalize Chromosomes that differ between real and validated lengths.
// Because validation can increase the length of the string for evaluation purposes - turning XX into X*X, for example - we won't use this right now.
//int ld = Math.Abs(c.GeneString.Length - c.GeneStringValidated.Length);
int ld = 0;
// On second thought, just penalize long strings.
int thresholdLength = 20;
int deltaLength = (c.GeneString.Length - thresholdLength);
ld = (deltaLength > 0) ? deltaLength : 0;
if (explain) Console.WriteLine("LD: " + ld);
if (ld != 0)
{
if (explain) Console.WriteLine("Length Penalty");
tFitness *= malusLength * ld;
c.FitnessBonus *= malusLength * ld;
}
//else
//{
// tFitness /= bonusValid;
// c.FitnessBonus /= bonusValid;
//}
//deltaLength = (c.GeneString.Length - bestEverChromosomeString.Length);
//ld = (deltaLength > 0) ? deltaLength : 0;
//if (explain) Console.WriteLine("LD: " + ld);
//if (ld != 0)
//{
// if (explain) Console.WriteLine("Length Penalty");
// tFitness *= malusLength * ld;
// c.FitnessBonus *= malusLength * ld;
//}
//deltaLength = (c.GeneString.Length - c.GeneStringValidated.Length);
//ld = (deltaLength > 0) ? deltaLength : 0;
//if (explain) Console.WriteLine("Silence: " + ld);
//if (ld != 0)
//{
// if (explain) Console.WriteLine("Silence Penalty");
// tFitness *= malusSilence * ld;
// c.FitnessBonus *= malusSilence * ld;
//}
// *0 or 0* is a pain.
// As is 0X or X0... so variables negated in such a fashion should result in penalty as well.
if (c.GeneString.Contains("*0") || c.GeneString.Contains("0*") || c.GeneStringValidated.Contains("*0") || c.GeneStringValidated.Contains("0*"))
{
if (explain) Console.WriteLine("*Zero Penalty");
tFitness *= malusTimesZero;
c.FitnessBonus *= malusTimesZero;
}
// For now, gotta change these when changing problem sets!
//char[] vars = { 'V', 'W', 'X', 'Y', 'Z' };
//char[] vars = { 'V', 'W', 'X' };
char[] vars = { 'X' };
char[] vars2 = { 'R', 'S', 'T', 'U', 'V', 'W', 'Y', 'Z' };
//List<char> varsAllList = new List<char>();
// Penalize 0X, X0, 1X, and X1.
foreach (char ch in "RSTUVWXYZ")
{
if (c.GeneString.Contains("0" + ch) || c.GeneString.Contains(ch + "0"))
{
if (explain) Console.WriteLine(ch + "*Zero Penalty");
tFitness *= malusTimesZero;
c.FitnessBonus *= malusTimesZero;
}
//varsAllList.Add(ch);
}
foreach (char ch in "RSTUVWXYZ")
{
if (
c.GeneString.Contains("1" + ch) || c.GeneString.Contains(ch + "1") ||
c.GeneString.Contains("1*" + ch) || c.GeneString.Contains(ch + "*1") ||
c.GeneString.Contains(ch + "/1")
)
{
if (explain) Console.WriteLine(ch + "*One Penalty");
tFitness *= malusTimesOne;
c.FitnessBonus *= malusTimesOne;
}
}
// I don't recall why I penalize this.
foreach (char ch1 in "RSTUVWYZ")
{
foreach (char ch2 in "RSTUVWXYZ")
{
if (c.GeneString.Contains(ch1.ToString() + ch2.ToString()) || c.GeneString.Contains(ch2.ToString() + ch1.ToString()))
{
if (explain) Console.WriteLine(ch1 + ch2 + " Penalty");
tFitness *= malusTimesZero;
c.FitnessBonus *= malusTimesZero;
}
}
}
//char[] varsAll = varsAllList.ToArray();
// Select against variables we don't want in the string.
foreach (char ch in vars2)
{
if (c.GeneString.Count(n => n.Equals(ch)) >= 1)
{
if (explain) Console.WriteLine(ch + " Penalty");
tFitness *= malusGene * c.GeneString.Count(n => n.Equals(ch));
c.FitnessBonus *= malusGene * c.GeneString.Count(n => n.Equals(ch));
}
}
foreach (char ch in vars)
{
// Penalize the Chromosome if our intended variables aren't included.
if (!c.GeneStringValidated.Contains(ch))
{
if (explain) Console.WriteLine("Required Var Penalty");
tFitness *= malusRequiredVariable;
c.FitnessBonus *= malusRequiredVariable;
}
// Reward if our intended variable(s) are.
else
{
if (explain) Console.WriteLine("Required Var Bonus");
tFitness /= bonusGene;
c.FitnessBonus /= bonusGene;
}
}
if (explain) Console.WriteLine(tFitness);
if (explain) Console.ReadLine();
if (tFitness > maxFitness) tFitness = maxFitness;
c.Fitness += tFitness;
}
return c.Fitness;
}
//public static Chromosome Crossover(Chromosome c, Chromosome b)
//{
// return Crossover(c, b, 1);
//}
/*
* Apparently, I very much need to review this code. As noted, my first attempt(s) at making this work properly, er, didn't.
* There may still be room for different approaches to crossover, but if I can verify that this one works as I intend,
* and that the fossil code doesn't, I can delete it entirely.
*
* Not that it can't be optimized.
*/
/// <summary>
/// Takes two parent Chromosomes, and returns a child Chromosome that derives from a crossover of both.
/// </summary>
/// <param name="c">One parent Chromosome.</param>
/// <param name="b">Another parent Chromosome.</param>
/// <returns>A child Chromosome.</returns>
public static List<Chromosome> Crossover(Chromosome c, Chromosome b)
{
List<Chromosome> children = new List<Chromosome>();
/*
* Meh. My first attempt at this sucked. :)
* Here, we take a random starting point in each Chromosome.
* Then, we calculate a random length, no longer than the distance between the starting point and the end.
*/
//int ics = r.Next(0, c.genes.Count);
//int ibs = r.Next(0, b.genes.Count);
//int icl = r.Next(1, c.genes.Count - ics);
//int ibl = r.Next(1, b.genes.Count - ibs);
int ic = r.Next(1, c.Genes.Count - 1);
int ib = r.Next(1, b.Genes.Count - 1);
//icl = Math.Max(icl, minLength);
//ibl = Math.Max(ibl, minLength);
//if (ics + icl > c.genes.Count)
//{
// ics -= Math.Abs(ics + icl - c.genes.Count);
//}
//if (ibs + ibl > b.genes.Count)
//{
// ibs -= Math.Abs(ibs + ibl - b.genes.Count);
//}
//Console.WriteLine("c.Count: " + c.genes.Count + "; b.Count: " + b.genes.Count);
//Console.WriteLine("c.Indices: " + ics + " " + (c.genes.Count - ics) + "; b.Indices: " + ibs + " " + (b.genes.Count - ibs));
//Console.WriteLine("c.Length: " + icl + "; b.Length: " + ibl);
//Console.WriteLine(c.GeneString);
//Console.WriteLine(b.GeneString);
// Get a section of Chromosome c.
ArrayList tcal1, tcal2; // = c.genes.GetRange(ics, icl);
// Get a section of Chromosome b.
ArrayList tbal1, tbal2; // = b.genes.GetRange(ibs, ibl);
//tcal = c.genes.GetRange(0, ics);
//tbal = b.genes.GetRange(ics, b.genes.Count - ics);
tcal1 = c.Genes.GetRange(0, ic);
tcal2 = c.Genes.GetRange(ic, c.Genes.Count - ic);
tbal1 = b.Genes.GetRange(0, ib);
tbal2 = b.Genes.GetRange(ib, b.Genes.Count - ib);
// Create an array list to combine the sections above.
ArrayList temp = new ArrayList();
ArrayList temp2 = new ArrayList();
//temp.AddRange(tcal1);
//temp.AddRange(tbal2);
//temp2.AddRange(tbal1);
//temp2.AddRange(tcal2);
int ir = r.Next(0, 2);
//// I could have this entirely randomized - pick part of one, part of the other, at random, and then put them together, at random.
//// For now, we'll take either a1 + b2, or b1 + a2.
ir = 0;
// I could do this in ternary, but this is just clearer.
switch (ir)
{
// Add c first.
case 0:
temp.AddRange(tcal1);
temp.AddRange(tbal2);
break;
// Add b first.
case 1:
temp.AddRange(tbal2);
temp.AddRange(tcal1);
break;
}
ir = r.Next(0, 2);
ir = 0;
switch (ir)
{
// Add c first.
case 0:
temp2.AddRange(tbal1);
temp2.AddRange(tcal2);
break;
// Add b first.
case 1:
temp2.AddRange(tcal2);
temp2.AddRange(tbal1);
break;
}
// Create a Chromosome from the resulting combination of ranges.
Chromosome t = new Chromosome(temp);
t.parentA = c.GeneString;
t.parentB = b.GeneString;
Chromosome t2 = new Chromosome(temp2);
t2.parentA = b.GeneString;
t2.parentB = c.GeneString;
//Console.WriteLine(c.genes.Count);
//Console.WriteLine(b.genes.Count);
//Console.WriteLine(t.genes.Count);
//Console.WriteLine(t2.genes.Count);
// Testing.
//Chromosome tc = new Chromosome(tcal);
//Chromosome tb = new Chromosome(tbal);
//Console.WriteLine(tc.GeneString);
//Console.WriteLine(tb.GeneString);
//Console.WriteLine(t.parentA);
//Console.WriteLine(t.parentB);
//Console.WriteLine(t.GeneString);
children.Add(t);
children.Add(t2);
return children;
}
static void DisplayChromosomeAt(Chromosome c, int x, int y)
{
ConsoleColor fc = Console.ForegroundColor;
ConsoleColor bc = Console.BackgroundColor;
Console.SetCursorPosition(x, y);
Console.WriteLine("Raw: " + c.GeneString);
Console.SetCursorPosition(x, y + 1);
Console.WriteLine("Processed: " + c.GeneStringValidated);
Console.SetCursorPosition(x, y + 2);
if (!c.IsValid) Console.ForegroundColor = ConsoleColor.Red;
Console.WriteLine("Evaluated: " + c.Result);
Console.SetCursorPosition(x, y + 3);
if (!c.IsValid) Console.ForegroundColor = ConsoleColor.Red;
if (c.Fitness <= minFitness * dict.Count) Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine("Fitness: " + c.Fitness + " " + c.FitnessBonus);
Console.ForegroundColor = fc;
Console.SetCursorPosition(0, y + 4);
Console.WriteLine("A: " + c.ParentA);
Console.SetCursorPosition(0, y + 5);
Console.WriteLine("B: " + c.ParentB);
}
//private void getData()
//{
// XmlSerializer xmlS = new XmlSerializer(typeof(List<Chromosome>));
// try
// {
// FileStream fs = new FileStream("C:/data.xml", FileMode.Open);
// population = (List<Chromosome>)xmlS.Deserialize(fs);
// fs.Close();
// historyPresent = true;
// }
// catch (Exception _e) {
// population = new List<Chromosome>();
// historyPresent = false;
// }
//}
//private void saveData()
//{
// XmlSerializer xmlS = new XmlSerializer(typeof(List<Chromosome>));
// try
// {
// FileStream fs = new FileStream("C:/data.xml", FileMode.OpenOrCreate);
// if(population.Count == 0)
// getNewPopulation();
// xmlS.Serialize(fs, population);
// fs.Flush();
// fs.Close();
// }
// catch (Exception _e)
// {
// population = new List<Chromosome>();
// }
//}
public void getNewPopulation(int problemSize, bool flag)
{
if (flag)
{
for (int i = 0; i < 10; i++)
{
Chromosome c = new Chromosome(problemSize);
for (int j = 0; j < c.mass.Count; j++)
{
c.mass[j] = rand.NextDouble();
c.force[j] = rand.NextDouble();
}
population.Add(c);
}
}
else
{
crossOver();
}
}
public void mutation(Chromosome c)
{
if(population.FirstOrDefault().mass.Count <= 2)
return;
int pointOfMutation = rand.Next(0, c.force.Count);
c.force[pointOfMutation] = rand.NextDouble();
c.mass[pointOfMutation] = rand.NextDouble();
}
public double fitnessFuction(Chromosome c)
{
double average = 0;
for (int i = 0; i < c.force.Count; i++)
{
//We want a good amount of force.
average += c.force[i];
//mass is not good.
average -= c.mass[i];
}
//averaging over all the values inside chromosome.
average = average / c.mass.Count;
//finding the max
if (average > max)
{
max = average;
bestFit = c;
Debug.Log("Max : " + average);
}
return average;
}
private void Initialize()
{
Lx = MathF.Ceiling(MathF.Log2((b - a) * MathF.Pow(10, q) + 1));
Ly = MathF.Ceiling(MathF.Log2((d - c) * MathF.Pow(10, q) + 1));
hx = (b - a) / (MathF.Pow(2, Lx) - 1);
hy = (d - c) / (MathF.Pow(2, Ly) - 1);
var points = new List <Vector2>();
float fitnessFunctionsSum = 0;
float stepX = (b - a) / 4f;
float stepY = (d - c) / 3f;
points.Add(new Vector2(a + stepX, c + stepY));
for (int i = 1; i <= _population.Count - 1; i++)
{
var point = points[i - 1];
if (i % 3 == 0)
{
points.Add(new Vector2(points[0].X, point.Y + stepY));
continue;
}
points.Add(new Vector2(point.X + stepX, point.Y));
}
for (int i = 0; i < points.Count; i++)
{
var chromosomeX = new Chromosome {
Parameter = points[i].X
};
var chromosomeY = new Chromosome {
Parameter = points[i].Y
};
var fitnessFunctionValue = _fitnessFunction(chromosomeX.Parameter, chromosomeY.Parameter);
fitnessFunctionsSum += fitnessFunctionValue;
var individual = new Individual
{
Сhromosomes = new List <Chromosome> {
chromosomeX, chromosomeY
},
FitnessFunctionValue = fitnessFunctionValue
};
_population.Individuals.Add(individual);
}
_population.FitnessFunctionSum = fitnessFunctionsSum;
if (CheckIfExistsFitnessFunctionValueLessZero())
{
RecalculateFitnessFunctionValues();
}
max = _population.FitnessFunctionSum / _population.Count;
Console.WriteLine(_population.FitnessFunctionSum / _population.Count);
}
public Chromosome ChaoticLS(Chromosome chrom) //Jia D , Zheng G , Khan M K . An effective memetic differential evolution algorithm based on chaotic local search[J]. Information ences, 2011, 181(15):3175-3187.
{
Chromosome betterChrom = Chromosome.Clone <Chromosome>(chrom); //use this method doing deep copy
double beta = myRandom.NextDouble(0.0, 1.0);
double u = 4.0, m = 1000;
double betac, lambda;
int Sl = (int)Math.Round(chrom.ChromosomeSize / 5.0);
if (Sl < 1)
{
Sl = 1;
}
int[] loc = myRandom.NextUnique(0, chrom.ChromosomeSize - 1, Sl);
//Random ran = new Random(Guid.NewGuid().GetHashCode());
while (true)
{
if (beta != 0.25 && beta != 0.5 && beta != 0.75)
{
break;
}
beta = myRandom.NextDouble(0.0, 1.0);
}
for (int i = 0; i < chrom.PopulationSize * 5; i++)
{
beta = u * beta * (1 - beta);
foreach (var j in loc)
{
betac = chrom.LowerBd[j] + beta * (chrom.UpperBd[j] - chrom.LowerBd[j]);
if (chrom.Fit > 0.0)
{
lambda = 1 - Math.Pow(Math.Abs((chrom.Fit - 1) / chrom.Fit), m);
}
else
{
lambda = 1 - Math.Pow(Math.Abs((chrom.Fit + 1) / chrom.Fit), m);
}
betterChrom.chromosome[j] = (1 - lambda) * chrom.chromosome[j] + lambda * betac;
}
betterChrom.CheckBoundary();
betterChrom.SetChromDecimalPlace();
betterChrom.SolveFitness();
if (betterChrom.Fit < chrom.Fit)
{
//Console.WriteLine("local search find a better individuals!");
break;
}
}
if (betterChrom.Fit < chrom.Fit)
{
return(betterChrom);
}
else
{
return(chrom);
}
}
public Chromosome LocalSearch(Chromosome chrom)
{
return(CLSMethodEvent?.Invoke(chrom));
}
public List <Chromosome> Execute()
{
//using select method
Selection SL = new Selection();
SL.SelectMethodEvent += SL.Select_Tournament;
//Chromosome result=SL.Select(Population pop);
//using cross method
Crossover CX = new Crossover();
CX.CrossMethodEvent += CX.CrossOver_MIX;
// List<Chromosome> result=CX.Cross(Chromosome mom, Chromosome dad);
//using mute method
Mutation MU = new Mutation();
MU.MuteMethodEvent += MU.DRM;
//Chromosome result = MU.Mute(Chromosome chrom);
//using chaotic local search
ChaoticLocalSearch LS = new ChaoticLocalSearch();
LS.CLSMethodEvent += LS.ChaoticLS;
#region All population evolutionary algebraic processes
//Iterative evolution
Random ran = new Random(Guid.NewGuid().GetHashCode());
List <Chromosome> result1 = new List <Chromosome>();
Chromosome result2 = new Chromosome();
int maxSameIt = 0;
for (int it = 0; it < MaxIteration; it++)
{
#region one population
for (int popi = 0; popi < NumPopulation; popi++)
{
// Update iteration times
PopsOfGa[popi].CurrentIteration = it;//Current iteration of evolution
PopsOfGaCX[popi].CurrentIteration = it;
PopsOfGaMU[popi].CurrentIteration = it;
//HistBestChromOfPops[popi].CurrentIteration = it;
for (int k = 0; k < PopsOfGa[popi].PopulationSize; k++)
{
PopsOfGa[popi].ChromsOfPop[k].CurrentIteration = it;
}
for (int k = 0; k < PopsOfGaCX[popi].ChromsOfPop.Count; k++)
{
PopsOfGaCX[popi].ChromsOfPop[k].CurrentIteration = it;
}
for (int k = 0; k < PopsOfGaMU[popi].ChromsOfPop.Count; k++)
{
PopsOfGaMU[popi].ChromsOfPop[k].CurrentIteration = it;
}
//select and cross
for (int k = 0; k < numOffspringsOfCross[popi] - 1; k = k + 2)
{
//select operator
int Chroms1Index = SL.Select(PopsOfGa[popi]);
int Chroms2Index = SL.Select(PopsOfGa[popi]);
//cross operator
result1 = CX.Cross(PopsOfGa[popi].ChromsOfPop[Chroms1Index], PopsOfGa[popi].ChromsOfPop[Chroms2Index]);
PopsOfGaCX[popi].ChromsOfPop.AddRange(result1);
}
PopsOfGaCX[popi].ChromsOfPop.RemoveRange(0, numOffspringsOfCross[popi]);
//Console.WriteLine("PopsOfGaCX before solve:");
//PopsOfGaCX[popi].ChromsOfPopCWFitAndChroms();
PopsOfGaCX[popi].ChromsOfPopSolveFit();// * solve every individuals fitness
//Console.WriteLine("PopsOfGaCX after solve:");
//PopsOfGaCX[popi].ChromsOfPopCWFitAndChroms();
//mute operator
for (int k = 0; k < numPointOfMute[popi]; k++)
{
int Chroms1IndexMu = ran.Next(0, PopsOfGa[popi].ChromsOfPop.Count);
result2 = MU.Mute(PopsOfGa[popi].ChromsOfPop[Chroms1IndexMu]);
PopsOfGaMU[popi].ChromsOfPop.Add(result2);
}
PopsOfGaMU[popi].ChromsOfPop.RemoveRange(0, numPointOfMute[popi]);
//Console.WriteLine("PopsOfGaMU before solve:");
//PopsOfGaMU[popi].ChromsOfPopCWFitAndChroms();
//Console.WriteLine("PopsOfGaMU after solve:");
PopsOfGaMU[popi].ChromsOfPopSolveFit();
//PopsOfGaMU[popi].ChromsOfPopCWFitAndChroms();
//combine three population
for (int iCx = 0; iCx < PopsOfGaCX[popi].PopulationSize; iCx++)
{
PopsOfGa[popi].ChromsOfPop.Add(Chromosome.Clone <Chromosome>(PopsOfGaCX[popi].ChromsOfPop[iCx]));
}
for (int iMu = 0; iMu < PopsOfGaMU[popi].PopulationSize; iMu++)
{
PopsOfGa[popi].ChromsOfPop.Add(Chromosome.Clone <Chromosome>(PopsOfGaMU[popi].ChromsOfPop[iMu]));
}
//Console.WriteLine($"PopsOfGa+PopsOfGaCX+PopsOfGaMU[{popi}] solve but before sort:");
//PopsOfGa[popi].ChromsOfPopCWFitAndChroms();
//Console.WriteLine($"sort:");
//sort(ascending) with fitness
PopsOfGa[popi].FitSort();//
//PopsOfGa[popi].ChromsOfPopCWFitAndChroms();
// remove the bad ones
PopsOfGa[popi].ChromsOfPop.RemoveRange(PopulationSize[popi], (numOffspringsOfCross[popi] + numPointOfMute[popi]));
//Console.WriteLine($"PopsOfGa[{popi}] after remove:");
//PopsOfGa[popi].ChromsOfPopCWFitAndChroms();
//Local search
PopsOfGa[popi].ChromsOfPop[0] = Chromosome.Clone <Chromosome>(LS.LocalSearch(PopsOfGa[popi].ChromsOfPop[0]));
//elitism in a population
if (HistBestChromOfPops[popi].Fit > PopsOfGa[popi].ChromsOfPop[0].Fit)
{
HistBestChromOfPops[popi] = Chromosome.Clone <Chromosome>(PopsOfGa[popi].ChromsOfPop[0]);
}
else
{
//PopsOfGa[popi].ChromsOfPop[0] = (Chromosome)DeepClone(HistBestChromOfPops[popi]);//elitism
PopsOfGa[popi].ChromsOfPop[0] = Chromosome.Clone <Chromosome>(HistBestChromOfPops[popi]);//elitism
}
HistBestChromOfPops[popi].CurrentIteration = it;
}
#endregion popi
//Take turns to swap the best:The best value of the next population gives the best value of the previous population
if (NumPopulation == 1)
{
//HistBestChromOfGa[it] = (Chromosome)DeepClone(HistBestChromOfPops[0]);
HistBestChromOfGa[it] = Chromosome.Clone <Chromosome>(HistBestChromOfPops[0]);
}
else
{
//Chromosome tmepChromo = (Chromosome)DeepClone(PopsOfGa[0].ChromsOfPop[0]);
Chromosome tmepChromo = Chromosome.Clone <Chromosome>(PopsOfGa[0].ChromsOfPop[0]);
for (int popi = 0; popi < NumPopulation; popi++)
{
if (HistBestChromOfGa[it].Fit > PopsOfGa[popi].ChromsOfPop[0].Fit)
{
//HistBestChromOfGa[it] = (Chromosome)DeepClone(PopsOfGa[popi].ChromsOfPop[0]);
HistBestChromOfGa[it] = Chromosome.Clone <Chromosome>(PopsOfGa[popi].ChromsOfPop[0]);
}
else
{
//PopsOfGa[popi].ChromsOfPop[0] = (Chromosome)DeepClone(HistBestChromOfGa[it]);
PopsOfGa[popi].ChromsOfPop[0] = Chromosome.Clone <Chromosome>(HistBestChromOfGa[it]);
}
//if (PopsOfGa[popi + 1] != null)
if ((popi + 1) < NumPopulation)
{
//PopsOfGa[popi].ChromsOfPop[0] = (Chromosome)DeepClone(PopsOfGa[popi + 1].ChromsOfPop[0]);
PopsOfGa[popi].ChromsOfPop[0] = Chromosome.Clone <Chromosome>(PopsOfGa[popi + 1].ChromsOfPop[0]);
}
if (popi == (NumPopulation - 1) && popi != 0)
{
//PopsOfGa[NumPopulation - 1].ChromsOfPop[0] = (Chromosome)DeepClone(tmepChromo);
PopsOfGa[NumPopulation - 1].ChromsOfPop[0] = Chromosome.Clone <Chromosome>(tmepChromo);
}
}
}
if (it > (int)Math.Round(MaxIteration * 0.3))
{
for (int itsame = 0; itsame < (int)Math.Round(MaxIteration * 0.3); itsame++)
{
if (Math.Abs(HistBestChromOfGa[it].Fit - HistBestChromOfGa[it - itsame - 1].Fit) < 1e-30)
{
maxSameIt++;
}
}
if (maxSameIt > (int)Math.Round(MaxIteration * 0.2))
{
break;
}
}
//
Console.WriteLine($"iteration:{HistBestChromOfGa[it].CurrentIteration},bestFit:{HistBestChromOfGa[it].Fit}");
foreach (double para in HistBestChromOfGa[it].chromosome)
{
Console.Write($"{para} ");
}
Console.WriteLine();
Console.WriteLine();
}
#endregion
return(HistBestChromOfGa);
// return globalBestChrom;
}
