/* Fitness evaluation works as follows: - Index and the character at that index must match Mutation works as follows: - Each character of each string in the population has SET.MUT% chance to undergo mutation Repopulation works as follows: - Fittest offspring is found [best] - The best is passed without modification to the next generation - Either first or second half of the best is chosen randomly for each offspring based on the chance of getting crossovered [sample] - Each offspring in the current generation does crossover with sample - Mutation is applied to the new generation Creator note: - program can be modified easily (for example you can add features like printing particular generation, or change the way repopulation, mutation, evaluation works or so) Created by Mustafa K. */ #include #include #include #include using namespace std; const string alph = "abcdefghijklmnopqrstuvwxyz "; // General.Settings // - only lower case is supported struct Settings { string TAR; // target string int LEN; // target string length int POP; // population size int MUT; // mutation rate int CRO; // crossover chance Settings() { // default settings TAR = ""; LEN = 0; POP = 0; MUT = 3; CRO = 70; } }SET; // short for settings // optional struct // for trivial informations struct Count { size_t crossOver; size_t generation; size_t mutation; Count() { // initial values crossOver = 0; generation = 1; mutation = 0; } }COUNT; string rand_str(int l); char rand_chr(); class GeneticAlgorithm { private: const size_t size; vector pop; // population vector fit; // fitness public: GeneticAlgorithm(size_t s, int l) : size(s) { for (size_t i=0; i temp) { temp = fit[i]; } } for (size_t i=0; i srand (time(0)); SET.TAR = "hey its me"; SET.LEN = SET.TAR.length(); SET.POP = 10; GeneticAlgorithm ga(SET.POP, SET.LEN); // <\setup> // --------------------------------- // while (!ga.found()) { ga.evaluate(); ga.rePopulate(); } // <\body> // --------------------------------- // ga.evaluate(); ga.print(); cout<< endl; cout<< "Cross-over count : "<< COUNT.crossOver << endl; cout<< "Mutation count : "<< COUNT.mutation << endl; cout<< "Found at "<< COUNT.generation << "th generation" << endl; cout<< ga.fittest() + 1; switch(ga.fittest() + 1) { case 1: cout<< "st offspring"; break; case 2: cout<< "nd offspring"; break; case 3: cout<< "rd offspring"; break; default: cout<< "th offspring"; break; } // <\final> return 0; } string rand_str (int l) { string str = ""; for (int i=0; i

Find Target w/ Genetic Algorithm