CODE YOUR ANSWER IN C#. I already know what a genetic algorithm looks like, I know how it works, I am asking for someone to do the code

to make a genetic algorithm work for N-Queens. My task is to make a visual N-queens board, in C# and Unity, that is solved by coding a genetic

algorithm (Generate, Evaluate, Sort, Cross-over, and Mutate) and a fitness function. The initial population size should be n = 8. The fitness function will

check the total number of attacks - sum up, for each piece, the number of pieces being attacked (some of these may be counted more than once and

that is fine). We want to find / favor solutions that minimize the number of attacks. Track the average fitness for each generation and graph those result

for 1000 generations. Track the best candidate seen thus far and graph that across the generations as well. Please code in C# and, Do NOT use AI for

this, it is easy to track and tell. Thank you so much for the assistance! ^^

Base Genetic Algorithm in C++ (Want the solution in C# please and thank you):

#include
#include
#include
#include
#include
#include

using namespace std;

int candidates = 6;
int candidateSize = 5;
float keep = 0.5;
float mutateRate = 0.2;
int limit = 10;

struct sample {
string code;
int value;
int eval;
};

int toDec(string decIn);
int f(int valueIn);
void evaluate(vector& sampleIn);
void sort(vector &sampleIn);
void crossover(vector &sampleIn);
void mutate(vector &samplesIn);

void generate(vector &samplesIn) {
for (int num = 0; num < candidates; num++) {
sample sample1;

//Generate a code based on candidateSize
//sample1.code = "010101";
string temp = "";
for (int i = 0; i < candidateSize; i++) {
temp += '0' + (rand() % 2);
}
sample1.code = temp;

//Determine value of string code
//sample1.value = 21;
sample1.value = toDec(sample1.code);

//Pass the value to the fitness function
sample1.eval = -1;
//sample1.eval = f(sample1.value);
samplesIn.push_back(sample1);
}
}

void showCandidates(vector samplesIn) {
for (int i = 0; i < samplesIn.size(); i++) {
cout << samplesIn[i].code << "\t" << samplesIn[i].value << "\t" << samplesIn[i].eval << endl;
}
cout << "=======================================================" << endl;
}

int main(int argc, char** argv) {
cout << "Genetic Algorithm" << endl;

//Initializaion
srand(time(NULL));

vector candidates;

//Generate
generate(candidates);
showCandidates(candidates);

for (int loop = 0; loop < limit; loop++) {
cout << "Generation " << loop << endl;

//Evaluate
evaluate(candidates);
showCandidates(candidates);

//Sort
sort(candidates);
showCandidates(candidates);

//Cross-over
crossover(candidates);
showCandidates(candidates);

//Mutate
mutate(candidates);
showCandidates(candidates);
}
}

int toDec(string decIn) {
int value = 0;
int power = 0;

for (int i = decIn.size() - 1; i >= 0; i--) {
value += (pow(2, power++) * (decIn[i] - '0'));
}

return value;
}

int f(int valueIn) {
return pow(valueIn,2);
}

void evaluate(vector &sampleIn) {
for (int i = 0; i < sampleIn.size(); i++) {
sampleIn[i].eval = f(sampleIn[i].value);
}
}

void sort(vector &sampleIn) {
for (int i = 0; i < sampleIn.size() - 1; i++) {
for (int j = i; j < sampleIn.size(); j++) {
if (sampleIn[i].eval < sampleIn[j].eval) {
//Switch
sample temp = sampleIn[i];
sampleIn[i] = sampleIn[j];
sampleIn[j] = temp;
}
}
}
}

void crossover(vector& sampleIn) {

//Make temp vector for new samples
vector newCandidates;

//Loop on keep percentage of population size
int keepCandidates = (candidates * keep);

//cout << "Keep " << keepCandidates << " candidates. " << endl;

sample sample1;
sample sample2;
for (int i = 0; i < candidates / 2; i++) {

//Choose two candidates from keep population
sample1 = sampleIn[(rand() % keepCandidates)];
//do {
sample2 = sampleIn[(rand() % keepCandidates)];
//} while (sample1.code == sample2.code);

sample newSample1;
sample newSample2;

//Choose cut location
int cut = rand() % (candidateSize - 2) + 1;

//Make two new candidates
newSample1.code = "";
newSample2.code = "";
for (int j = 0; j < candidateSize; j++) {
if (j < cut) {
newSample1.code += sample1.code[j];
newSample2.code += sample2.code[j];
}
else {
newSample1.code += sample2.code[j];
newSample2.code += sample1.code[j];
}
}

//Save them
newSample1.value = toDec(newSample1.code);
newSample2.value = toDec(newSample2.code);
newSample1.eval = -1;
newSample2.eval = -1;
newCandidates.push_back(newSample1);
newCandidates.push_back(newSample2);

}

//Replace the old candidate list with the new one
for (int i = 0; i < candidates; i++) {
sampleIn[i] = newCandidates[i];
}

}

void mutate(vector& samplesIn) {

//My mutate function
for (int i = 0; i < samplesIn.size(); i++) {
if (((float)(rand() % 100) / (float)100) < mutateRate) {
int location = rand() % samplesIn[i].code.length();
if (samplesIn[i].code[location] == '0') {
samplesIn[i].code[location] = '1';
}
else if (samplesIn[i].code[location] == '1') {
samplesIn[i].code[location] = '0';
}
samplesIn[i].value = toDec(samplesIn[i].code);
}
}

//reset their strings
for (int i = 0; i < samplesIn.size(); i++) {
samplesIn[i].value = toDec(samplesIn[i].code);
}
}

void generationStatus(vector& samplesIn) {
float totalFitness = 0;
int largestNum = 0;

for (int i = 0; i < samplesIn.size(); i++) {
//noting the total fitness and then will be average by dividing by the size at the end of the loop
totalFitness += samplesIn[i].eval;

//finding the largest value of the generation
if (i == 0) {
largestNum = samplesIn[i].value;
}
else if (largestNum < samplesIn[i].value) {
largestNum = samplesIn[i].value;
}
}
totalFitness /= samplesIn.size();
cout << "The largest value of this generation is " << largestNum << "\nThe average fitness of this generation is " << totalFitness << endl << endl;
}