public void Test50StateCodes()
{
Fitness.UseRegexLength = true;
var wanted = new[]
{
"AL", "AK", "AZ", "AR", "CA",
"CO", "CT", "DE", "FL", "GA",
"HI", "ID", "IL", "IN", "IA",
"KS", "KY", "LA", "ME", "MD",
"MA", "MI", "MN", "MS", "MO",
"MT", "NE", "NV", "NH", "NJ",
"NM", "NY", "NC", "ND", "OH",
"OK", "OR", "PA", "RI", "SC",
"SD", "TN", "TX", "UT", "VT",
"VA", "WA", "WV", "WI", "WY"
};
const string alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";
var unwanted = (from a in alphabet
from b in alphabet
where !wanted.Contains($"{a}{b}")
select $"{a}{b}").Concat(
from i in alphabet select $"{i}").ToArray();
var customOperators = new FnMutateDelegate[]
{
genes => MutateToCharacterSetLeft(genes, wanted),
MutateToCharacterSet,
genes => MutateAddWanted(genes, wanted)
};
FindRegex(wanted, unwanted, 120, customOperators);
}
public void TestStateCodes()
{
Fitness.UseRegexLength = true;
var wanted = new[] { "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND" };
var unwanted = "ABCDEFGHIJKLMNOPQRSTUVWXYZ".Select(v => $"N{v}").Where(st => !wanted.Contains(st))
.ToArray();
var customOperators = new FnMutateDelegate[]
{
genes => MutateToCharacterSetLeft(genes, wanted)
};
FindRegex(wanted, unwanted, 11, customOperators);
}
public void TornamentTest()
{
var minGenes = 10;
var maxGenes = 20;
var geneset = CreateGeneSet();
var watch = Stopwatch.StartNew();
void FnDisplay(List <Rule> genes, int wins, int ties, int losses, int generation)
{
Console.WriteLine("-- generation {0} --", generation);
Display(
new Chromosome <Rule, Fitness>(genes, new Fitness(wins, ties, losses, genes.Count), Strategy.None),
watch);
}
var mutationRoundCounts = new List <int> {
1
};
var mutationOperators = new FnMutateDelegate[]
{
genes => MutateAdd(genes, geneset),
genes => MutateReplace(genes, geneset),
MutateRemove,
MutateSwapAdjacent,
MutateMove,
};
void FnMutate(List <Rule> genes) =>
Mutate(genes, x => new Fitness(0, 0, 0, 0), mutationOperators, mutationRoundCounts);
List <Rule> FnCrossover(IReadOnlyList <Rule> parent, IReadOnlyList <Rule> doner)
{
var child = parent.Take(parent.Count / 2).Concat(doner.Skip(doner.Count / 2)).ToList();
FnMutate(child);
return(child);
}
List <Rule> FnCreate() =>
Rand.RandomSample(geneset, Rand.Random.Next(minGenes, maxGenes));
int FnSortKey(List <Rule> genes, int wins, int ties, int losses) => - 1000 * losses - ties + 1 / genes.Count;
var unused =
Genetic <Rule, Fitness> .Tournament(FnCreate, FnCrossover, PlayOneOnOne, FnDisplay, FnSortKey, 13);
}
public void PerfectKnowledgeTest()
{
var minGenes = 10;
var maxGenes = 20;
var geneset = CreateGeneSet();
var watch = Stopwatch.StartNew();
void FnDisplay(Chromosome <Rule, Fitness> candidate, int?length) =>
Display(candidate, watch);
Fitness FnGetFitness(IReadOnlyList <Rule> genes) =>
GetFitness(genes);
var mutationRoundCounts = new List <int> {
1
};
var mutationOperators = new FnMutateDelegate[]
{
genes => MutateAdd(genes, geneset),
genes => MutateReplace(genes, geneset),
MutateRemove,
MutateSwapAdjacent,
MutateMove,
};
void FnMutate(List <Rule> genes) =>
Mutate(genes, FnGetFitness, mutationOperators, mutationRoundCounts);
List <Rule> FnCrossover(IReadOnlyList <Rule> parent, IReadOnlyList <Rule> doner)
{
var child = parent.Take(parent.Count / 2).Concat(doner.Skip(doner.Count / 2)).ToList();
FnMutate(child);
return(child);
}
List <Rule> FnCreate() =>
Rand.RandomSample(geneset, Rand.Random.Next(minGenes, maxGenes));
var optimalFitness = new Fitness(620, 120, 0, 11);
var best = Genetic <Rule, Fitness> .GetBest(FnGetFitness, minGenes, optimalFitness, null, FnDisplay, FnMutate,
FnCreate, 500, 20, FnCrossover);
Assert.IsTrue(optimalFitness.CompareTo(best.Fitness) <= 0);
}
public void TestEvenLength()
{
var wanted = new[]
{
"00", "01", "10", "11", "0000", "0001", "0010", "0011",
"0100", "0101", "0110", "0111", "1000", "1001", "1010",
"1011", "1100", "1101", "1110", "1111"
};
var unwanted = new[]
{
"0", "1", "000", "001", "010", "011", "100", "101",
"110", "111", ""
};
var customOperators = new FnMutateDelegate[]
{
MutateToCharacterSet
};
FindRegex(wanted, unwanted, 10, customOperators);
}
