public string Duplicate(string toMatch)
{
var solver = new GeneticSolver
{
MaxSecondsWithoutImprovement = 1
};
int geneCount = toMatch.Length;
Func<string, int> getFitness = child =>
{
int matches = Enumerable.Range(0, geneCount)
.Count(x => child[x] != toMatch[x]);
return matches;
};
string geneSet = new String(toMatch.Distinct().ToArray());
var stopwatch = new Stopwatch();
stopwatch.Start();
Action<int, int, string, string> displayCurrentBest =
(generation, fitness, genes, strategy) =>
Console.WriteLine("generation\t{0} fitness\t{1} {2}\telapsed: {3}",
generation.ToString().PadLeft(5, ' '),
fitness.ToString().PadLeft(TotalWidth(toMatch), ' '),
genes,
stopwatch.Elapsed);
string result = solver.GetBest(toMatch.Length,
geneSet,
getFitness,
displayCurrentBest);
Console.WriteLine(result);
return result;
}
public string Solve()
{
Func<string, int> getFitness = child =>
{
var queenLocations = new HashSet<Point>(child.Select(x => x.ToPoint(GeneSet, BoardWidth)));
int fitness = queenLocations.Sum(x => CountQueensAttacked(GetAttackablePoints(x), queenLocations));
fitness += 10000 * (GeneCount - queenLocations.Count);
return fitness;
};
var stopwatch = new Stopwatch();
stopwatch.Start();
Action<int, int, string, string> displayCurrentBest =
(generation, fitness, genes, strategy) =>
{
var board = new char?[BoardHeight, BoardWidth];
foreach (var queenLocation in genes.Select(x => x.ToPoint(GeneSet, BoardWidth)))
{
board[queenLocation.X, queenLocation.Y] = 'Q';
}
for (int i = 0; i < BoardHeight; i++)
{
for (int j = 0; j < BoardWidth; j++)
{
Console.Write(board[i, j] ?? '.');
Console.Write(' ');
}
Console.WriteLine();
}
Console.WriteLine("generation\t{0} fitness\t{1} elapsed: {2}",
generation.ToString().PadLeft(5, ' '),
fitness.ToString().PadLeft(2, ' '),
stopwatch.Elapsed);
};
var geneticSolver = new GeneticSolver
{
GetCanonicalGenes = GetCanonicalGenes,
MaxSecondsWithoutImprovement = 5
};
string result = geneticSolver.GetBest(GeneCount,
GeneSet,
getFitness,
displayCurrentBest);
Console.WriteLine(result);
return getFitness(result) == 0
? GetCanonicalGenes(result)
: null;
}
public string Solve(IRouteSource routeSource, int maxSecondsWithoutImprovement)
{
if (routeSource.OptimalRoute != null)
{
Console.WriteLine("Expect optimal route " + routeSource.OptimalRoute
+ " to have fitness " + CalculateRouteLength(routeSource.OptimalRoute, routeSource));
}
var stopwatch = new Stopwatch();
stopwatch.Start();
var geneticSolver = new GeneticSolver
{
GetCanonicalGenes = GetCanonicalGenes,
MaxSecondsWithoutImprovement = maxSecondsWithoutImprovement,
NumberOfParentLines = 4
};
string result = geneticSolver.GetBest(
routeSource.GeneSet.Length,
routeSource.GeneSet,
child => CalculateRouteLength(child, routeSource),
(generation, fitness, genes, strategy) => Display(generation, fitness, genes, strategy, stopwatch));
Console.WriteLine(GetCanonicalGenes(result));
return GetCanonicalGenes(result);
}