public void StreamWithInitialGenotypes()
{
var problem = Problem.Of(
a => a,
Codec.Of(
() => Genotype.Of(IntegerChromosome.Of(0, 1000)),
g => g.Gene.Allele
)
);
const int genotypeCount = 10;
const int max = 1000;
var genotypes = IntRange.Of(1, genotypeCount)
.Select(i => IntegerChromosome.Of(IntegerGene.Of(max, 0, max)))
.Select(i => Genotype.Of(i))
.ToImmutableSeq();
var engine = Engine.Builder(problem).Build();
var result = engine.Stream(genotypes)
.Take(1)
.ToBestEvolutionResult();
long maxCount = result.GetPopulation().Count(pt => pt.GetFitness() == max);
Assert.True(maxCount >= genotypeCount, $"{maxCount} >= {genotypeCount}");
}
/// <summary>
/// Mutates a gene by bitwise mutation.
/// </summary>
/// <param name="gene"></param>
/// <returns></returns>
public static Gene Mutate(Gene gene)
{
if (gene is BinaryGene)
{
BinaryGene g = (BinaryGene)gene.Clone();
g.Value = !(BinaryGene)gene;
return(g);
}
else if (gene is DoubleGene)
{
DoubleGene g = (DoubleGene)gene.Clone();
byte[] bytes = BitConverter.GetBytes(g.Value);
BitArray ba = new BitArray(bytes);
int p = Utils.Rand.Next(ba.Length);
ba.Set(p, !ba[p]);
ba.CopyTo(bytes, 0);
g.Value = BitConverter.ToDouble(bytes, 0);
return(g);
}
else if (gene is IntegerGene)
{
IntegerGene g = (IntegerGene)gene.Clone();
byte[] bytes = BitConverter.GetBytes(g.Value);
BitArray ba = new BitArray(bytes);
int p = Utils.Rand.Next(ba.Length);
ba.Set(p, !ba[p]);
ba.CopyTo(bytes, 0);
g.Value = BitConverter.ToInt32(bytes, 0);
return(g);
}
return((Gene)gene.Clone()); // default
}
/// <summary>
/// Mutates a gene by applying boundary mutation.
/// </summary>
/// <param name="gene">An IntegerGene or DoubleGene to be mutated.</param>
/// <returns></returns>
public static Gene Mutate(Gene gene)
{
if (gene is IntegerGene)
{
IntegerGene ig = (IntegerGene)gene;
if (genX.Utils.Rand.NextDouble() < 0.5)
{
ig.Value = ig.Descriptor.MaxValue;
}
else
{
ig.Value = ig.Descriptor.MinValue;
}
}
else if (gene is DoubleGene)
{
DoubleGene dg = (DoubleGene)gene;
if (genX.Utils.Rand.NextDouble() < 0.5)
{
dg.Value = dg.Descriptor.MaxValue;
}
else
{
dg.Value = dg.Descriptor.MinValue;
}
}
else
{
throw new ArgumentException(
"Boundary mutation may only apply to IntegerGene or DoubleGene genes.",
"gene"
);
}
return(gene);
}
public double GetObjective(Chromosome c)
{
double distance = 0;
int currentPosition = 0;
int initialPosition = 0;
for (int i = 0; i < c.Genes.Length; i++)
{
IntegerGene ig = (IntegerGene)c.Genes[i];
if (i == 0)
{
initialPosition = cities[ig.Label];
currentPosition = cities[ig.Label];
}
else
{
distance += Math.Abs(cities[ig.Label] - currentPosition);
currentPosition = cities[ig.Label];
}
}
IntegerGene lastGene = (IntegerGene)c.Genes[c.Genes.Length - 1];
distance += Math.Abs(cities[lastGene.Label] - initialPosition);
return(distance);
}
public void ItCanMutateRandomly()
{
var numberGene = new IntegerGene(_r);
Assert.AreEqual(1, numberGene.GetNumberOfGeneAttributes());
numberGene.Mutate(MutationStrategy.Random);
Assert.AreEqual(8, numberGene.Value);
}
public void ItCanBoundaryMutate()
{
var numberGene = new IntegerGene(_r);
Assert.AreEqual(1, numberGene.GetNumberOfGeneAttributes());
numberGene.Mutate(MutationStrategy.Boundary);
Assert.AreEqual(9, numberGene.Value);
}
public double LinearDiophantineObjective(Chromosome c)
{
double r = 0;
for (int i = 0; i < Coefficients.Length; i++)
{
IntegerGene g = (IntegerGene)c.Genes[i];
r += ((double)Coefficients[i] * (double)g.Value);
}
return(System.Math.Abs((double)Result - r));
}
public TSPGenome(GeneticAlgorithm owner, City[] cities)
{
var organism = new int[cities.Length];
var taken = new bool[cities.Length];
for (int i = 0; i < organism.Length; i++)
{
taken[i] = false;
}
for (int i = 0; i < organism.Length - 1; i++)
{
int icandidate;
do
{
icandidate = (int)(ThreadSafeRandom.NextDouble() * organism.Length);
} while (taken[icandidate]);
organism[i] = icandidate;
taken[icandidate] = true;
if (i == organism.Length - 2)
{
icandidate = 0;
while (taken[icandidate])
{
icandidate++;
}
organism[i + 1] = icandidate;
}
}
pathChromosome = new Chromosome();
Chromosomes.Add(pathChromosome);
for (int i = 0; i < organism.Length; i++)
{
var gene = new IntegerGene();
gene.Value = organism[i];
pathChromosome.Genes.Add(gene);
}
Organism = organism;
Encode();
}
/// <summary>
/// 遺伝子を生成します
/// </summary>
/// <param name="chromosomesType"></param>
/// <returns></returns>
public static IntegerGene GenerateGene(ChromosomesType chromosomesType, Func <List <int>, double> calcFunc, int geneLength)
{
var gene = new IntegerGene
{
GenerationNum = 0,
CalcFunc = calcFunc,
Fittness = 0,
};
switch (chromosomesType)
{
case ChromosomesType.Binary:
gene.Chromosomes = GenerateBinaryChromosomes(geneLength);
break;
case ChromosomesType.Permutaion:
gene.Chromosomes = GeneratePermutationChromosomes(geneLength);
break;
}
return(gene);
}
private static EvolutionResult <IntegerGene, int> NewResult(Optimize opt, int value)
{
const int length = 1000;
Func <Genotype <IntegerGene>, int> F = delegate(Genotype <IntegerGene> gt)
{
return(gt.Gene.Allele);
};
var pop = new Population <IntegerGene, int>(length);
for (var i = 0; i < length; ++i)
{
var gt = Genotype.Of(IntegerChromosome.Of(
IntegerGene.Of(value, 0, length)
));
pop.Add(Phenotype.Of(gt, 1, F));
}
Shuffle(pop, RandomRegistry.GetRandom());
return(EvolutionResult.Of(opt, pop, 0, 0, EvolutionDurations.Zero, 0, 0, 0));
}
public void BestWorstPhenotype()
{
const int length = 100;
Func <Genotype <IntegerGene>, int> F = delegate(Genotype <IntegerGene> gt)
{
return(gt.Gene.Allele);
};
var population = new Population <IntegerGene, int>(length);
for (var i = 0; i < length; ++i)
{
var gt = Genotype.Of(IntegerChromosome.Of(
IntegerGene.Of(i, 0, length)
));
population.Add(Phenotype.Of(gt, 1, F));
}
Shuffle(population, RandomRegistry.GetRandom());
var maxResult = EvolutionResult.Of(
Optimize.Maximum, population,
0, 0, EvolutionDurations.Zero, 0, 0, 0
);
Assert.Equal(length - 1, maxResult.GetBestFitness());
Assert.Equal(0, maxResult.GetWorstFitness());
var minResult = EvolutionResult.Of(
Optimize.Minimum, population,
0, 0, EvolutionDurations.Zero, 0, 0, 0
);
Assert.Equal(0, minResult.GetBestFitness());
Assert.Equal(length - 1, minResult.GetWorstFitness());
}
public void CompareTo()
{
const int length = 100;
Func <Genotype <IntegerGene>, int> F = delegate(Genotype <IntegerGene> gt)
{
return(gt.Gene.Allele);
};
var small = new Population <IntegerGene, int>(length);
for (var i = 0; i < length; ++i)
{
var gt = Genotype.Of(IntegerChromosome.Of(
IntegerGene.Of(i, 0, length)
));
small.Add(Phenotype.Of(gt, 1, F));
}
Shuffle(small, RandomRegistry.GetRandom());
var big = new Population <IntegerGene, int>(length);
for (var i = 0; i < length; ++i)
{
var gt = Genotype.Of(IntegerChromosome.Of(
IntegerGene.Of(i + length, 0, length)
));
big.Add(Phenotype.Of(gt, 1, F));
}
Shuffle(big, RandomRegistry.GetRandom());
var smallMaxResult = EvolutionResult.Of(
Optimize.Maximum, small,
0, 0, EvolutionDurations.Zero, 0, 0, 0
);
var bigMaxResult = EvolutionResult.Of(
Optimize.Maximum, big,
0, 0, EvolutionDurations.Zero, 0, 0, 0
);
Assert.True(smallMaxResult.CompareTo(bigMaxResult) < 0);
Assert.True(bigMaxResult.CompareTo(smallMaxResult) > 0);
Assert.True(smallMaxResult.CompareTo(smallMaxResult) == 0);
Assert.True(bigMaxResult.CompareTo(bigMaxResult) == 0);
var smallMinResult = EvolutionResult.Of(
Optimize.Minimum, small,
0, 0, EvolutionDurations.Zero, 0, 0, 0
);
var bigMinResult = EvolutionResult.Of(
Optimize.Minimum, big,
0, 0, EvolutionDurations.Zero, 0, 0, 0
);
Assert.True(smallMinResult.CompareTo(bigMinResult) > 0);
Assert.True(bigMinResult.CompareTo(smallMinResult) < 0);
Assert.True(smallMinResult.CompareTo(smallMinResult) == 0);
Assert.True(bigMinResult.CompareTo(bigMinResult) == 0);
}
public void ItCanCreateWithRandomValues()
{
var gene = new IntegerGene(_r);
Assert.AreEqual(3, gene.Value);
}
public void ItCanDetermineTheNumberOfGeneAttributesOnAGene()
{
var numberGene = new IntegerGene(_r);
Assert.AreEqual(1, numberGene.GetNumberOfGeneAttributes());
}