Ok so I finished my code n queens genetics in c# but I keep getting these compiler errors even though I changed the code several times
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace NQueen1
{
class Program
{
private const int sSize = 75; // Population size at start.
private const int mTest = 1000; // Arbitrary number of test cycles.
private const double pMating = 0.7; // Probability of two chromosomes mating. Range: 0.0 < MATING_PROBABILITY < 1.0
private const double rMutation = 0.001; // Mutation Rate. Range: 0.0 < MUTATION_RATE < 1.0
private const int minS = 10; // Minimum parents allowed for selection.
private const int MaxS = 50; // Maximum parents allowed for selection. Range: MIN_SELECT < MAX_SELECT < START_SIZE
private const int offSpring = 20; // New offspring created per generation. Range: 0 < OFFSPRING_PER_GENERATION < MAX_SELECT.
private const int minRandom = 8; // For randomizing starting chromosomes
private const int maxShuffles = 20;
private const int maxPBC = 4; // Maximum Position-Based Crossover points. Range: 0 < PBC_MAX < 8 (> 8 isn't good).
private const int maxLength = 10; // chess board width.
private static int epoch = 0;
private static int childCount = 0;
private static int nextMutation = 0; // For scheduling mutations.
private static int mutations = 0;
private static List<Chromosome> population = new List<Chromosome>();
private static void algorithm()
{
int popSize = 0;
Chromosome thisChromo = null;
bool done = false;
initializeChromosomes();
mutations = 0;
nextMutation = getRandomNumber(0, (int)Math.Round(1.0 / rMutation));
while (!done)
{
popSize = population.Count;
for (int i = 0; i < popSize; i++)
{
thisChromo = population[i];
if ((thisChromo.conflicts() == 0) || epoch == mTest)
{
done = true;
}
}
getFitness();
rouletteSelection();
mating();
prepNextEpoch();
epoch++;
// This is here simply to show the runtime status.
Console.WriteLine("Epoch: " + epoch);
}
Console.WriteLine("done.");
if (epoch != rMutation)
{
popSize = population.Count;
for (int i = 0; i < popSize; i++)
{
thisChromo = population[i];
if (thisChromo.conflicts() == 0)
{
printSolution(thisChromo);
}
}
}
Console.WriteLine("Completed " + epoch + " epochs.");
Console.WriteLine("Encountered " + mutations + " mutations in " + childCount + " offspring.");
return;
}
private static void getFitness()
{
// Lowest errors = 100%, Highest errors = 0%
int popSize = population.Count;
Chromosome thisChromo = null;
double bestScore = 0;
double worstScore = 0;
// The worst score would be the one with the highest energy, best would be lowest.
worstScore = population[maximum()].conflicts();
// Convert to a weighted percentage.
bestScore = worstScore - population[minimum()].conflicts();
for (int i = 0; i < popSize; i++)
{
thisChromo = population[i];
thisChromo.fitness((worstScore - thisChromo.conflicts()) * 100.0 / bestScore);
}
return;
}
private static void rouletteSelection()
{
int j = 0;
int popSize = population.Count;
double genT = 0.0;
double selT = 0.0;
int maximumToSelect = getRandomNumber(minS, MaxS);
double rouletteSpin = 0.0;
Chromosome thisChromo = null;
Chromosome thatChromo = null;
bool done = false;
for (int i = 0; i < popSize; i++)
{
thisChromo = population[i];
genT += thisChromo.fitness();
}
genT *= 0.01;
for (int i = 0; i < popSize; i++)
{
thisChromo = population[i];
thisChromo.selectionProbability(thisChromo.fitness() / genT);
}
for (int i = 0; i < maximumToSelect; i++)
{
rouletteSpin = getRandomNumber(0, 99);
j = 0;
selT = 0;
done = false;
while (!done)
{
thisChromo = population[j];
selT += thisChromo.selectionProbability();
if (selT >= rouletteSpin)
{
if (j == 0)
{
thatChromo = population[j];
}
else if (j >= popSize - 1)
{
thatChromo = population[popSize - 1];
}
else
{
thatChromo = population[j - 1];
}
thatChromo.selected(true);
done = true;
}
else
{
j++;
}
}
}
return;
}
// This is where you can choose between options:
// To choose between crossover options, uncomment one of:
// partiallyMappedCrossover(),
// positionBasedCrossover(), while keeping the other two commented out.
private static void mating()
{
int getRand = 0;
int parentA = 0;
int parentB = 0;
int newIndex1 = 0;
int newIndex2 = 0;
Chromosome newChromo1 = null;
Chromosome newChromo2 = null;
for (int i = 0; i < offSpring; i++)
{
parentA = chooseParent();
// Test probability of mating.
getRand = getRandomNumber(0, 100);
if (getRand <= pMating * 100)
{
parentB = chooseParent(parentA);
newChromo1 = new Chromosome();
newChromo2 = new Chromosome();
population.Add(newChromo1);
newIndex1 = population.IndexOf(newChromo1);
population.Add(newChromo2);
newIndex2 = population.IndexOf(newChromo2);
// Choose either, or both of these:
partialCrossover(parentA, parentB, newIndex1, newIndex2);
//positionBasedCrossover(parentA, parentB, newIndex1, newIndex2);
if (childCount - 1 == nextMutation)
{
exchangeMutation(newIndex1, 1);
}
else if (childCount == nextMutation)
{
exchangeMutation(newIndex2, 1);
}
population[newIndex1].computeConflicts();
population[newIndex2].computeConflicts();
childCount += 2;
// Schedule next mutation.
if (childCount % (int)Math.Round(1.0 / rMutation) == 0)
{
nextMutation = childCount + getRandomNumber(0, (int)Math.Round(1.0 / rMutation));
}
}
} // i
return;
}
private static void partialCrossover(int chromA, int chromB, int child1, int child2)
{
int j = 0;
int item1 = 0;
int item2 = 0;
int pos1 = 0;
int pos2 = 0;
Chromosome thisChromo = population[chromA];
Chromosome thatChromo = population[chromB];
Chromosome newChromo1 = population[child1];
Chromosome newChromo2 = population[child2];
int crossPoint1 = getRandomNumber(0, maxLength - 1);
int crossPoint2 = getExclusiveRandomNumber(maxLength - 1, crossPoint1);
if (crossPoint2 < crossPoint1)
{
j = crossPoint1;
crossPoint1 = crossPoint2;
crossPoint2 = j;
}
// Copy Parent genes to offspring.
for (int i = 0; i < maxLength; i++)
{
newChromo1.data(i, thisChromo.data(i));
newChromo2.data(i, thatChromo.data(i));
}
for (int i = crossPoint1; i <= crossPoint2; i++)
{
// Get the two items to swap.
item1 = thisChromo.data(i);
item2 = thatChromo.data(i);
// Get the items// positions in the offspring.
for (j = 0; j < maxLength; j++)
{
if (newChromo1.data(j) == item1)
{
pos1 = j;
}
else if (newChromo1.data(j) == item2)
{
pos2 = j;
}
} // j
// Swap them.
if (item1 != item2)
{
newChromo1.data(pos1, item2);
newChromo1.data(pos2, item1);
}
// Get the items// positions in the offspring.
for (j = 0; j < maxLength; j++)
{
if (newChromo2.data(j) == item2)
{
pos1 = j;
}
else if (newChromo2.data(j) == item1)
{
pos2 = j;
}
} // j
// Swap them.
if (item1 != item2)
{
newChromo2.data(pos1, item1);
newChromo2.data(pos2, item2);
}
} // i
return;
}
private static void positionCrossover(int chromA, int chromB, int child1, int child2)
{
int k = 0;
int numPoints = 0;
int[] tempArray1 = new int[maxLength];
int[] tempArray2 = new int[maxLength];
bool matchFound = false;
Chromosome thisChromo = population[chromA];
Chromosome thatChromo = population[chromB];
Chromosome newChromo1 = population[child1];
Chromosome newChromo2 = population[child2];
// Choose and sort the crosspoints.
numPoints = getRandomNumber(0, maxPBC);
int[] crossPoints = new int[numPoints];
int negativeNancy = -1;
for (int i = 0; i < numPoints; i++)
{
crossPoints[i] = getRandomNumber(0, maxLength - negativeNancy, crossPoints);
} // i
// Get non-chosens from parent 2
k = 0;
for (int i = 0; i < maxLength; i++)
{
matchFound = false;
for (int j = 0; j < numPoints; j++)
{
if (thatChromo.data(i) == thisChromo.data(crossPoints[j]))
{
matchFound = true;
}
} // j
if (matchFound == false)
{
tempArray1[k] = thatChromo.data(i);
k++;
}
} // i
// Insert chosens into child 1.
for (int i = 0; i < numPoints; i++)
{
newChromo1.data(crossPoints[i], thisChromo.data(crossPoints[i]));
}
// Fill in non-chosens to child 1.
k = 0;
for (int i = 0; i < maxLength; i++)
{
matchFound = false;
for (int j = 0; j < numPoints; j++)
{
if (i == crossPoints[j])
{
matchFound = true;
}
} // j
if (matchFound == false)
{
newChromo1.data(i, tempArray1[k]);
k++;
}
} // i
// Get non-chosens from parent 1
k = 0;
for (int i = 0; i < maxLength; i++)
{
matchFound = false;
for (int j = 0; j < numPoints; j++)
{
if (thisChromo.data(i) == thatChromo.data(crossPoints[j]))
{
matchFound = true;
}
} // j
if (matchFound == false)
{
tempArray2[k] = thisChromo.data(i);
k++;
}
} // i
// Insert chosens into child 2.
for (int i = 0; i < numPoints; i++)
{
newChromo2.data(crossPoints[i], thatChromo.data(crossPoints[i]));
}
// Fill in non-chosens to child 2.
k = 0;
for (int i = 0; i < maxLength; i++)
{
matchFound = false;
for (int j = 0; j < numPoints; j++)
{
if (i == crossPoints[j])
{
matchFound = true;
}
} // j
if (matchFound == false)
{
newChromo2.data(i, tempArray2[k]);
k++;
}
} // i
return;
}
private static void exchangeMutation(int index, int exchanges)
{
int i = 0;
int tempData = 0;
Chromosome thisChromo = null;
int gene1 = 0;
int gene2 = 0;
bool done = false;
thisChromo = population[index];
while (!done)
{
gene1 = getRandomNumber(0, maxLength - 1);
gene2 = getExclusiveRandomNumber(maxLength - 1, gene1);
// Exchange the chosen genes.
tempData = thisChromo.data(gene1);
thisChromo.data(gene1, thisChromo.data(gene2));
thisChromo.data(gene2, tempData);
if (i == exchanges)
{
done = true;
}
i++;
}
mutations++;
return;
}
private static int chooseParent()
{
// Overloaded function, see also "chooseparent(ByVal parentA As Integer)".
int parent = 0;
Chromosome thisChromo = null;
bool done = false;
while (!done)
{
// Randomly choose an eligible parent.
parent = getRandomNumber(0, population.Count - 1);
thisChromo = population[parent];
if (thisChromo.selected() == true)
{
done = true;
}
}
return parent;
}
{
// Overloaded function, see also "chooseparent()".
int parent = 0;
Chromosome thisChromo = null;
bool done = false;
while (!done)
{
// Randomly choose an eligible parent.
parent = getRandomNumber(0, population.Count - 1);
if (parent != parentA)
{
thisChromo = population[parent];
if (thisChromo.selected() == true)
{
done = true;
}
}
}
return parent;
}
private static void prepNextEpoch()
{
int popSize = 0;
Chromosome thisChromo = null;
// Reset flags for selected individuals.
popSize = population.Count;
for (int i = 0; i < popSize; i++)
{
thisChromo = population[i];
thisChromo.selected(false);
}
return;
}
private static void printSolution(Chromosome bestSolution)
{
string[][] board = RectangularArrays.ReturnRectangularStringArray(maxLength, maxLength);
// Clear the board.
for (int x = 0; x < maxLength; x++)
{
for (int y = 0; y < maxLength; y++)
{
board[x][y] = "";
}
}
for (int x = 0; x < maxLength; x++)
{
board[x][bestSolution.data(x)] = "Q";
}
// Display the board.
Console.WriteLine("Board:");
for (int y = 0; y < maxLength; y++)
{
for (int x = 0; x < maxLength; x++)
{
if (string.ReferenceEquals(board[x][y], "Q"))
{
Console.Write("Q ");
}
else
{
Console.Write(". ");
}
}
Console.Write("\n");
}
return;
}
private static int getRandomNumber(int low, int high)
{
return (int)Math.Round((high - low) * (new Random()).NextDouble() + low);
}
private static int getExclusiveRandomNumber(int high, int except)
{
bool done = false;
int getRand = 0;
while (!done)
{
getRand = (new Random()).Next(high);
if (getRand != except)
{
done = true;
}
}
return getRand;
}
private static int getRandomNumber(int low, int high, int[] except)
{
bool done = false;
int getRand = 0;
if (high != low)
{
while (!done)
{
done = true;
getRand = (int)Math.Round((high - low) * (new Random()).NextDouble() + low);
for (int i = 0; i < except.Length; i++) //UBound(except)
{
if (getRand == except[i])
{
done = false;
}
} // i
}
return getRand;
}
else
{
return high; // or low (it doesn't matter).
}
}
private static int minimum()
{
// Returns an array index.
int popSize = 0;
Chromosome thisChromo = null;
Chromosome thatChromo = null;
int winner = 0;
bool foundNewWinner = false;
bool done = false;
while (!done)
{
foundNewWinner = false;
popSize = population.Count;
for (int i = 0; i < popSize; i++)
{
if (i != winner)
{ // Avoid self-comparison.
thisChromo = population[i];
thatChromo = population[winner];
if (thisChromo.conflicts() < thatChromo.conflicts())
{
winner = i;
foundNewWinner = true;
}
}
}
if (foundNewWinner == false)
{
done = true;
}
}
return winner;
}
private static int maximum()
{
// Returns an array index.
int popSize = 0;
Chromosome thisChromo = null;
Chromosome thatChromo = null;
int winner = 0;
bool foundNewWinner = false;
bool done = false;
while (!done)
{
foundNewWinner = false;
popSize = population.Count;
for (int i = 0; i < popSize; i++)
{
if (i != winner)
{ // Avoid self-comparison.
thisChromo = population[i];
thatChromo = population[winner];
if (thisChromo.conflicts() > thatChromo.conflicts())
{
winner = i;
foundNewWinner = true;
}
}
}
if (foundNewWinner == false)
{
done = true;
}
}
return winner;
}
private static void initializeChromosomes()
{
int shuffles = 0;
Chromosome newChromo = null;
int chromoIndex = 0;
for (int i = 0; i < sSize; i++)
{
newChromo = new Chromosome();
population.Add(newChromo);
chromoIndex = population.IndexOf(newChromo);
// Randomly choose the number of shuffles to perform.
shuffles = getRandomNumber(minRandom, maxShuffles);
exchangeMutation(chromoIndex, shuffles);
population[chromoIndex].computeConflicts();
}
return;
}
private class Chromosome
{
internal int[] mData = new int[maxLength];
internal double mFitness = 0.0;
internal bool mSelected = false;
internal double mSelectionProbability = 0.0;
internal int mConflicts = 0;
public Chromosome()
{
for (int i = 0; i < maxLength; i++)
{
this.mData[i] = i;
}
return;
}
public virtual void computeConflicts()
{
int x = 0;
int y = 0;
int tempx = 0;
int tempy = 0;
//string[][] board = new string[MAX_LENGTH][MAX_LENGTH];
string[][] board = RectangularArrays.ReturnRectangularStringArray(maxLength, maxLength);
int conflicts = 0;
int[] dx = new int[] { -1, 1, -1, 1 };
int[] dy = new int[] { -1, 1, 1, -1 };
bool done = false;
// Clear the board.
for (int i = 0; i < maxLength; i++)
{
for (int j = 0; j < maxLength; j++)
{
board[i][j] = "";
}
}
for (int i = 0; i < maxLength; i++)
{
board[i][this.mData[i]] = "Q";
}
// Walk through each of the Queens and compute the number of conflicts.
for (int i = 0; i < maxLength; i++)
{
x = i;
y = this.mData[i];
// Check diagonals.
for (int j = 0; j <= 3; j++)
{
tempx = x;
tempy = y;
done = false;
while (!done)
{
tempx += dx[j];
tempy += dy[j];
if ((tempx < 0 || tempx >= maxLength) || (tempy < 0 || tempy >= maxLength))
{
done = true;
}
else
{
if (board[tempx][tempy].ToString().ToUpper().Equals("Q"))// ignore the case of 2 strings
{
conflicts++;
}
}
}
}
}
this.mConflicts = conflicts;
}
public virtual void conflicts(int value)
{
this.mConflicts = value;
return;
}
public virtual int conflicts()
{
return this.mConflicts;
}
public virtual double selectionProbability()
{
return mSelectionProbability;
}
public virtual void selectionProbability(double SelProb)
{
mSelectionProbability = SelProb;
return;
}
public virtual bool selected()
{
return mSelected;
}
public virtual void selected(bool sValue)
{
mSelected = sValue;
return;
}
public virtual double fitness()
{
return mFitness;
}
public virtual void fitness(double score)
{
mFitness = score;
return;
}
public virtual int data(int index)
{
return mData[index];
}
public virtual void data(int index, int value)
{
mData[index] = value;
return;
}
} // Chromosome
static void Main(string[] args)
{
algorithm();
return;
}
}
}
This is the second code here:
namespace NQueen1
{
internal static class RectangularArrays
{
internal static string[][] ReturnRectangularStringArray(int size1, int size2)
{
string[][] newArray = new string[size1][];
for (int array1 = 0; array1 < size1; array1++)
{
newArray[array1] = new string[size2];
}
return newArray;
}
}
}
THe Error:
Unhandled Exception: System.ArgumentOutOfRangeException: Index was out of range. Must be non-negative and less than the size of the collection. Parameter name: index at System.ThrowHelper.ThrowArgumentOutOfRangeException(ExceptionArgument argument, ExceptionResource resource) at System.Collections.Generic.List`1.get_Item(Int32 index) at NQueen1.Program.rouletteSelection() in C:\Users\Inspiron\Documents\coid\NQueen1\NQueen1\Program.cs:line 143 at NQueen1.Program.algorithm() in C:\Users\Inspiron\Documents\coid\NQueen1\NQueen1\Program.cs:line 56 at NQueen1.Program.Main(String[] args) in C:\Users\Inspiron\Documents\coid\NQueen1\NQueen1\Program.cs:line 841
I have no clue why its throwing off these errors I tried about almost everything I could think of to fix it
