Initial Commit

.gitignore

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+main.exe
+.vscode

README.md

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-# BinaryCodedGeneticAlgorithm
+# Binary Coded Genetic Algorithm
+

main.cpp

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+#include <iostream>
+#include <cstdlib>
+
+#define ITERATION 100
+#define POPULATIONCOUNT 20
+#define CROSSOVERCHANCE .5
+#define MUTATIONCHANCE .02
+#define CROSSOVERCOUNTPERITERATION 10
+#define POPULATIONSIZE 200
+#define BITCOUNT 12
+
+const float ValuePeriod = (float)(1 << BITCOUNT);
+const unsigned int ValuePeriodMask = (1 << BITCOUNT) - 1;
+
+struct Chromosome
+{
+    unsigned int x1;
+    unsigned int x2;
+};
+
+unsigned int GetRandomValue() { return rand() & ValuePeriodMask; }
+float GetRandomPercentage()
+{
+    // RAND_MAX in Windows causes the rand values to be between 0 and 32767
+    // So to overcome we get two random numbers and bitshift the one of them
+    int random = rand() << 15 | rand();
+    return float(random % 1000000) / 1000000.0;
+}
+
+float GetX1(unsigned int value) { return -1.5 + value * (4.0 / ValuePeriod); }
+float GetX2(unsigned int value) { return value * (5.0 / ValuePeriod); }
+
+float GetFitnessScore(const Chromosome &chromosome)
+{
+    float x1 = GetX1(chromosome.x1);
+    float x2 = GetX2(chromosome.x2);
+
+    float score = 40.0;
+    score -= 4.5 * x1;
+    score += 4.0 * x2;
+    score -= x1 * x1;
+    score -= 2.0 * x2 * x2;
+    score += 2.0 * x1 * x2;
+    score -= x1 * x1 * x1 * x1;
+    score += 2.0 * x1 * x1 * x2;
+    return score;
+}
+
+void Initialize(Chromosome &chromosome)
+{
+    chromosome.x1 = GetRandomValue();
+    chromosome.x2 = GetRandomValue();
+}
+
+unsigned int InvertBits(unsigned int &value) { return (~value) & ValuePeriodMask; }
+unsigned int GetRandomBits(float chance)
+{
+    unsigned int result = 0;
+
+    for (size_t i = 0; i < BITCOUNT; i++)
+        if (GetRandomPercentage() < chance)
+            result += 1 << i;
+
+    return result & ValuePeriodMask;
+}
+
+void Mutate(Chromosome &chromosome)
+{
+    unsigned int mutationBitsX1 = GetRandomBits(MUTATIONCHANCE);
+    unsigned int mutationBitsX2 = GetRandomBits(MUTATIONCHANCE);
+
+    chromosome.x1 ^= mutationBitsX1;
+    chromosome.x2 ^= mutationBitsX2;
+}
+
+Chromosome Crossover(Chromosome &chromosomeLeft, Chromosome &chromosomeRight)
+{
+    Chromosome result = Chromosome();
+
+    unsigned int crossoverBitsX1 = GetRandomBits(CROSSOVERCHANCE);
+    unsigned int crossoverBitsX2 = GetRandomBits(CROSSOVERCHANCE);
+
+    result.x1 = chromosomeLeft.x1 & crossoverBitsX1;
+    result.x1 |= chromosomeRight.x1 & InvertBits(crossoverBitsX1);
+
+    result.x2 = chromosomeLeft.x2 & crossoverBitsX2;
+    result.x2 |= chromosomeRight.x2 & InvertBits(crossoverBitsX2);
+
+    return result;
+}
+
+void UpdateFitnessScores(Chromosome *populationPointer, float *fitnessPointer)
+{
+    for (size_t i = 0; i < POPULATIONSIZE; i++)
+        *(fitnessPointer++) = GetFitnessScore(*(populationPointer++));
+}
+
+Chromosome *GetRandomChromosome(Chromosome *populationPointer) { return populationPointer + rand() % POPULATIONSIZE; }
+Chromosome *GetRandomCumulativeChromosome(Chromosome *populationPointer, float *fitnessPointer)
+{
+    float sumOfScores = 0.0;
+    float cumulativeFloat = 0.0;
+    float randomPoint = GetRandomPercentage();
+    size_t i;
+
+    for (i = 0; i < POPULATIONSIZE; i++)
+        sumOfScores += *(fitnessPointer + i);
+
+    for (i = 0; i < POPULATIONSIZE; i++)
+    {
+        cumulativeFloat += *(fitnessPointer + i) / sumOfScores;
+
+        if (randomPoint <= cumulativeFloat)
+            return populationPointer + i;
+    }
+
+    return populationPointer;
+}
+
+Chromosome *GetFittest(Chromosome *populationPointer, float *fitnessPointer)
+{
+    unsigned int fittestIndex = 0;
+    for (size_t i = 0; i < POPULATIONSIZE; i++)
+        if (*(fitnessPointer + i) > *(fitnessPointer + fittestIndex))
+            fittestIndex = i;
+
+    return populationPointer + fittestIndex;
+}
+
+Chromosome *GetElitistOffSpring(Chromosome *fittest, Chromosome *populationPointer)
+{
+    Chromosome *offspring = nullptr;
+
+    do
+        offspring = GetRandomChromosome(populationPointer);
+    while (fittest == offspring);
+
+    return offspring;
+}
+
+void SortScores(float *scores)
+{
+    float temp;
+    for (size_t i = 0; i < POPULATIONCOUNT; i++)
+        for (size_t j = i + 1; j < POPULATIONCOUNT; j++)
+            if (*(scores + i) > *(scores + j))
+            {
+                temp = *(scores + i);
+                *(scores + i) = *(scores + j);
+                *(scores + j) = temp;
+            }
+}
+
+int main()
+{
+    Chromosome population[POPULATIONSIZE];
+    float fitnessScores[POPULATIONSIZE];
+    float fitnessScoreRecord[POPULATIONCOUNT];
+    Chromosome *offspring = nullptr;
+    Chromosome *left = nullptr;
+    Chromosome *right = nullptr;
+    Chromosome *fittest = nullptr;
+    int i;
+    int generation;
+    int crossoverCounter;
+    int mutationIndex;
+    int populationCounter;
+
+    srand(0);
+
+    for (populationCounter = 0; populationCounter < POPULATIONCOUNT; populationCounter++)
+    {
+        for (i = 0; i < POPULATIONSIZE; i++)
+            Initialize(population[i]);
+        UpdateFitnessScores(population, fitnessScores);
+        fittest = GetFittest(population, fitnessScores);
+
+        for (generation = 0; generation < ITERATION; generation++)
+        {
+            for (crossoverCounter = 0; crossoverCounter < CROSSOVERCOUNTPERITERATION; crossoverCounter++)
+            {
+                offspring = GetElitistOffSpring(fittest, population);
+
+                left = GetRandomCumulativeChromosome(population, fitnessScores);
+                right = GetRandomCumulativeChromosome(population, fitnessScores);
+                (*offspring) = Crossover(*left, *right);
+            }
+
+            // Mutate all except the Fittest
+            for (mutationIndex = 0; mutationIndex < POPULATIONSIZE; mutationIndex++)
+                if (population + mutationIndex != fittest)
+                    Mutate(*(population + mutationIndex));
+
+            UpdateFitnessScores(population, fitnessScores);
+            fittest = GetFittest(population, fitnessScores);
+
+            // std::cout << "Fittest Score: " << GetFitnessScore(*fittest) << "\n";
+            // std::cout << "x1: " << GetX1(fittest->x1) << "\n";
+            // std::cout << "x2: " << GetX2(fittest->x2) << "\n";
+            // std::cout << "-----End of Generation " << generation << "-----\n";
+        }
+        fitnessScoreRecord[populationCounter] = GetFitnessScore(*fittest);
+    }
+
+    SortScores(fitnessScoreRecord);
+
+    std::cout << "Population Size: " << POPULATIONSIZE << "\n";
+    std::cout << "Population Count: " << POPULATIONCOUNT << "\n";
+    std::cout << "Iteration Count: " << ITERATION << "\n";
+    std::cout << "Crossover Per Iteration: " << CROSSOVERCOUNTPERITERATION << "\n";
+    std::cout << "Crossover Ratio: " << CROSSOVERCHANCE << "\n";
+    std::cout << "Mutation Ratio: " << MUTATIONCHANCE << "\n";
+    std::cout << "Bits Per Value: " << BITCOUNT << "\n";
+    std::cout << "-----------------\n";
+    std::cout << "Best: " << fitnessScoreRecord[POPULATIONCOUNT - 1] << "\n";
+    std::cout << "Median: " << fitnessScoreRecord[(POPULATIONCOUNT - 1) / 2] << "\n";
+    std::cout << "Worst: " << fitnessScoreRecord[0] << "\n";
+
+    return 0;
+}

makefile

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+main.exe: main.cpp
+	g++ -o main.exe main.cpp