//key 19
public string Decrypt()
{
string result = string.Empty;
for (int j = 0; j < 256; j++)
{
string text = string.Empty;
foreach (char c in source)
{
text += (char)(c ^ j);
}
if (CipherFitness.NewEvaluate(text) > 70)
{
result += "key: " + (char)j + "\nresult string:\n" + text;
break;
}
}
return(result);
}
public static void Main(string[] args)
{
string plaintextFilename = "text_sample.txt";
string logFilename = "log.txt";
string key = "cipherkey";
int populationSize = 100;
int offspring = 10;
float mutationProbability = 0.25f;
float crossoverProbability = 0.85f;
int maxGenerations = 500;
using (StreamWriter w = File.AppendText($"../../../logs/{logFilename}"))
{
//read in plaintext
string plaintext = File.ReadAllText($"../../../statistics/text/{plaintextFilename}").ToLower();
//init cipher and encipher plaintext
ICipher cipher = new VigenereCipher(plaintext);
cipher.Encipher(key);
Console.WriteLine(cipher.CipherText);
//init genetic alorithm operators
//use tournament selection to minimize the likelyhood of staying in a lokal optima
var selection = new TournamentSelection(5);
//crossover the solution candidates by choosing a interval of the key and swap the interval
var crossover = new TwoPointCrossover();
//mutate the solution candidates with the own implemented mutation operator
var mutation = new CipherMutation();
//evaluate the fitness of the solution candidates with the own implemented fitness function
var fitness = new CipherFitness(cipher);
//initalize the solution candidates with the own implemented solution candidate implementation
var chromosome = new CipherChromosome(key.Length);
//initialize a population
var population = new Population(populationSize, populationSize + offspring, chromosome);
//initialize the genetic algorithm with the above initialized operators
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
//set mutation and crossover probabilities
ga.MutationProbability = mutationProbability;
ga.CrossoverProbability = crossoverProbability;
//specify the algorithm termination with max generations or a fitness threshold
ga.Termination = new OrTermination(new GenerationNumberTermination(maxGenerations), new FitnessThresholdTermination(0.99));
var latestFitness = 0.0;
//hang into the GenerationRan event which gets called by the library after each generation ran.
ga.GenerationRan += (sender, e) =>
{
//select the best solution candidate in the current generation
var bestChromosome = ga.BestChromosome as CipherChromosome;
//if fitness in current generation is better than fitness in previous generations
if (bestChromosome.Fitness != latestFitness)
{
latestFitness = bestChromosome.Fitness.Value;
string genText = String.Format("Generation {0}: Best solution found is Key:{1} with {2} fitness.", ga.GenerationsNumber, bestChromosome.ToString(), bestChromosome.Fitness);
Console.WriteLine(genText);
w.WriteLine(genText);
}
};
string gaText = $"------Plaintextfile:{plaintextFilename}, Key:{key}------\n------Genetic Algorithm Settings: Populationsize:{populationSize}, Mutation:{mutationProbability}, Crossover:{crossoverProbability}------";
Console.WriteLine(gaText);
w.WriteLine(gaText);
//start ga
ga.Start();
var finalSolution = ga.BestChromosome as CipherChromosome;
string doneText = $"GA done in {ga.GenerationsNumber} generations.\n Best solution found is Key:{finalSolution.ToString()} (L={finalSolution.ToString().Length}) with {finalSolution.Fitness} fitness. ";
string decipheredText = cipher.Decipher(finalSolution.ToString());
Console.WriteLine(doneText + "\n" + decipheredText);
w.WriteLine(doneText + "\n" + decipheredText);
}
}
