public IFitness CreateFitness()
{
var db = "";
foreach (var file in Directory.GetFiles(@"C:\Users\giacomelli\Downloads\wn3.1.dict\dict\dbfiles", "*.*"))
{
db += File.ReadAllText(file).ToUpperInvariant();
}
var f = new GhostwriterFitness();
f.EvaluateFunc = (text) =>
{
//Console.WriteLine("Evaluating text: {0}", text);
var words = text.Split(new string[] { " " }, StringSplitOptions.RemoveEmptyEntries);
var points = 0.0;
var wordsNotFound = 0.0;
foreach (var word in words)
{
try
{
if (db.Contains(word))
{
points += word.Length;
}
else
{
wordsNotFound += word.Length * 2;
}
}
catch
{
points--;
continue;
}
}
if (points > 100)
{
points = 100;
}
else if (points < 0)
{
points = 0;
}
var fitness = (points / wordsNotFound) / 100.0;
//onsole.WriteLine("Fitness: {0}", fitness);
return(fitness);
};
return(f);
}
public IFitness CreateFitness()
{
var f = new GhostwriterFitness();
f.EvaluateFunc = (text) =>
{
var minDistance = m_quotes.Min(q => LevenshteinDistance(q, text));
return(1 - (minDistance / 100f));
};
return(f);
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new GhostwriterChromosome(4, new string[] { "The", "C#", "Genetic", "Algorithm", "library" });
var fitness = new GhostwriterFitness((t) => t.Length);
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(5);
ga.Start();
Assert.NotNull(ga.BestChromosome);
}