/// <summary>
/// Initializes this instance.
/// </summary>
public override void Initialize()
{
Console.WriteLine("Function arguments and expected result: arg1,arg2=expected result.");
Console.WriteLine("Sample1: 1,2,3=6");
Console.WriteLine("Sample2: 2,3,4=24");
Console.WriteLine("When finish, type ENTER to start the GA.");
m_inputs = new List <FunctionBuilderInput>();
do
{
var parts = Console.ReadLine().Split('=');
if (parts.Length != 2)
{
Console.WriteLine("Max number of operations?");
m_maxOperations = Convert.ToInt32(Console.ReadLine());
break;
}
var arguments = parts[0].Split(',');
var input = new FunctionBuilderInput(
arguments.Select(a => Convert.ToDouble(a)).ToList(),
Convert.ToDouble(parts[1]));
m_inputs.Add(input);
}while (true);
m_fitness = new FunctionBuilderFitness(m_inputs.ToArray());
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = new FunctionBuilderFitness(
new FunctionBuilderInput(
new double[] { 1, 2 },
3)
,
new FunctionBuilderInput(
new double[] { 2, 3 },
5)
);
var chromosome = new FunctionBuilderChromosome(fitness.AvailableOperations, 3);
var population = new Population(100, 200, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new OrTermination(new FitnessThresholdTermination(0), new TimeEvolvingTermination(TimeSpan.FromSeconds(15)));
ga.Start();
var bestChromosome = ga.BestChromosome as FunctionBuilderChromosome;
Assert.AreEqual(0.0, bestChromosome.Fitness.Value);
var actual = fitness.GetFunctionResult(
bestChromosome.BuildFunction(),
new FunctionBuilderInput(new double[] { 3, 4 }, 7)
);
Assert.AreEqual(7, actual);
}
static void Main(string[] args)
{
// Problem: Simple addition
// 1 + 1 = 2
// 2 + 2 = 4
// 3 + 2 = 5
var myInputs = new List <FunctionBuilderInput>
{
new FunctionBuilderInput(new List <double> {
1, 1
}, 2),
new FunctionBuilderInput(new List <double> {
2, 2
}, 4),
new FunctionBuilderInput(new List <double> {
3, 2
}, 5)
};
var myFitness = new FunctionBuilderFitness(myInputs.ToArray());
var myChromosome = new FunctionBuilderChromosome(myFitness.AvailableOperations, 5);
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = myFitness;
var chromosome = myChromosome;
var population = new Population(100, 200, chromosome)
{
GenerationStrategy = new PerformanceGenerationStrategy()
};
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new FitnessThresholdTermination(0)
};
ga.GenerationRan += delegate
{
Console.Clear();
var bestChromosome = ga.Population.BestChromosome;
Console.WriteLine("Generations: {0}", ga.Population.GenerationsNumber);
Console.WriteLine("Fitness: {0,10}", bestChromosome.Fitness);
Console.WriteLine("Time: {0}", ga.TimeEvolving);
Console.WriteLine("Speed (gen/sec): {0:0.0000}", ga.Population.GenerationsNumber / ga.TimeEvolving.TotalSeconds);
var best = bestChromosome as FunctionBuilderChromosome;
Console.WriteLine("Function: {0}", best.BuildFunction());
};
ga.Start();
Console.WriteLine("Evolved.");
Console.ReadKey();
}
public void Evaluate_AllResultsEquals_MaxFitness()
{
var target = new FunctionBuilderFitness(
new FunctionBuilderInput(new double[] { 1 }, 1),
new FunctionBuilderInput(new double[] { 2 }, 2));
var c = new FunctionBuilderChromosome(target.AvailableOperations, 2);
c.ReplaceGene(0, new Gene("A"));
c.ReplaceGene(1, new Gene(""));
var actual = target.Evaluate(c);
Assert.AreEqual(0, actual);
}
public void Evaluate_InvalidFunction_WorstFitness()
{
var target = new FunctionBuilderFitness(
new FunctionBuilderInput(new double[] { 1 }, 2),
new FunctionBuilderInput(new double[] { 1 }, 3));
var c = new FunctionBuilderChromosome(target.AvailableOperations, 2);
c.ReplaceGene(0, new Gene("-"));
c.ReplaceGene(1, new Gene(""));
var actual = target.Evaluate(c);
Assert.AreEqual(double.MinValue, actual);
}
