Little example of how genetic algorithms can work in c++

GeneticAlgorithm.cpp

#include <algorithm>
#include <chrono>
#include <cmath>
#include <iostream>
#include <thread>
#include <time.h>
#include <vector>

// lets create a population
struct Member {
	std::string DNA;
	size_t Fitness;
};

struct Population {
	// this will create a population with 50k
	// members, lets choose a lower number first
	// to see the progress, works fine...
	std::vector<Member> Members = std::vector<Member>(5000);
};

int main()
{
	// for this example we are going to use a string as a kind of "goal" to
	// achieve
	std::string DNA =
	"I am a DNA string that needs to be found, shall happen soon!";

	bool sequenceFound{false};
	std::string sequenceString{""};

	// We are going to use this for mutation of genes
	// 1000 = 100% 1 = .01% mutation rate
	int mutationRate{25};

	srand(static_cast<unsigned int>(time(NULL)));

	// create a population and initialize it with random DNA, also set the
	// fitness to 0
	Population Pop;

	for (auto &member : Pop.Members) {
		member.DNA.resize(DNA.size());
		for (size_t j = 0; j < DNA.size(); j++) {
			member.DNA.at(j) =
			static_cast<char>(std::abs(rand() % 96 + 32));
		}
		member.Fitness = 0;
	}

	std::clog << "initializing done !" << std::endl;
	// we will use a variable to keep track of how many generations there have
	// been
	int generation = 0;

	// our actuall work will be inside this loop, it will stop when the DNA
	// sequence has fully evolved to the target
	while (!sequenceFound) {
		generation++;

		// clear out the fitness here and then reevaluate for each
		// member, also check if fitness has reached maximum
		for (auto &member : Pop.Members) {
			member.Fitness = 0;
			for (size_t j = 0; j < member.DNA.size(); j++) {
				if (member.DNA.at(j) == DNA.at(j)) {
					member.Fitness += 10;
				}
			}
			if (member.Fitness == DNA.size() * 10)
			sequenceFound = true;
			sequenceString = member.DNA;
		}

		// now lets sort the population by fitness, from highest to lowest
		std::sort(Pop.Members.begin(), Pop.Members.end(),
			  [](Member const &a, Member &b) {
			  return a.Fitness > b.Fitness;
			  });

		// select x amount of highest fitness members to pair from, lets use
		// 2 parents in this case
		std::vector<Member> Parents{Pop.Members.at(0), Pop.Members.at(1)};

		// lets do some gene permutation and mating
		for (auto &member : Pop.Members) {

			for (size_t j = 0; j < member.DNA.size(); j++) {

				// lets use an equal chance to take each parents
				// gene (on a per gene basis)
				size_t TempSelection = static_cast<size_t>(
				static_cast<size_t>(rand()) % Parents.size());
				member.DNA.at(j) =
				Parents.at(TempSelection).DNA.at(j);

				// dont forget to apply random mutation based on
				// our value from above
				if (rand() % 1000 < mutationRate) {
					member.DNA.at(j) = static_cast<char>(
					std::abs(rand() % 96 + 32));
				}
			}
		}

		// lets print some stuff
		std::cout << "generation : " << generation
			  << " Highest Fitness : " << Parents.at(0).Fitness
			  << " With Sequence : " << Parents.at(0).DNA.c_str()
			  << std::endl;
	}

	std::cout << "generation " << generation << " Evolved to the full sequence"
		  << std::endl;
	std::cout << "sequenceFound :" << sequenceFound << std::endl;
	std::cout << "sequence DNA :" << sequenceString << std::endl;
	return 0;
}

// done... :)