private void RemoveGene(int i)
{
Array.Copy(Genes, i + 1, Genes, i, GenesLength - (i + 1));
var move = new TMove();
move.Decode((int)Genes[GenesLength - 1]);
move.Axis = (move.Axis + 1) % 3;
Genes[GenesLength - 1] = move.Encode();
}
private void button1_Click(object sender, EventArgs e)
{
for (int i = 0; i < 25; i++)
{
var code = Rnd.Next(9 * RubikCube.N);
var move = new TMove();
move.Decode(code);
Moves.Add(move);
}
MoveTimer.Start();
}
public void LoadSolutions()
{
var fs = new FileStream(SolutionPath, FileMode.OpenOrCreate);
var reader = new BinaryReader(fs);
while (reader.BaseStream.Position < reader.BaseStream.Length)
{
var key = reader.ReadString();
var movesCount = reader.ReadInt32();
var moves = new List <TMove>();
for (int i = 0; i < movesCount; i++)
{
var move = new TMove();
move.Decode(reader.ReadInt32());
moves.Add(move);
}
Solutions.Add(key, moves);
}
fs.Close();
}
public double OnEvaluate(TChromosome specimen)
{
var cube = new RubikCube(RubikCube);
var bestFitness = double.MaxValue;
for (int len = 0; len < specimen.Genes.Length; len++)
{
var move = new TMove();
move.Decode((int)specimen.Genes[len]);
cube.MakeMove(move);
var fitness = cube.Evaluate();
if (fitness < bestFitness)
{
bestFitness = fitness;
(specimen as RubikGenome).MovesCount = len + 1;
}
}
specimen.Fitness = bestFitness;
return(bestFitness);
}
public void Correct()
{
for (int i = 0; i < GenesLength; i++)
{
var move = new TMove();
move.Decode((int)Genes[i]);
for (int j = i - 1; j >= 0; j--)
{
var pMove = new TMove();
pMove.Decode((int)Genes[j]);
if (move.Axis != pMove.Axis)
{
break;
}
if (move.SegNo == pMove.SegNo)
{
RemoveGene(i);
i--;
var angle = (pMove.Angle + move.Angle + 2) % 4;
if (angle == 0)
{
RemoveGene(j);
i--;
}
else
{
pMove.Angle = angle - 1;
Genes[j] = pMove.Encode();
}
break;
}
}
}
}
void Solve()
{
var best = new RubikGenome();
best.Fitness = HighScore;
foreach (var solution in Solutions)
{
var genome = new RubikGenome();
var cube = new RubikCube(RubikCube);
for (int i = 0; i < solution.Value.Count; i++)
{
var move = solution.Value[i];
cube.MakeMove(move);
genome.Genes[i] = move.Encode();
}
genome.Fitness = cube.Evaluate();
if (genome.Fitness < best.Fitness)
{
best = genome;
}
}
if (best.Fitness >= HighScore)
{
TChromosome.GenesLength = 30;
TChromosome.MinGenes = new double[TChromosome.GenesLength];
TChromosome.MaxGenes = new double[TChromosome.GenesLength];
for (int i = 0; i < TChromosome.MaxGenes.Length; i++)
{
TChromosome.MaxGenes[i] = 9 * RubikCube.N;
}
TGA <RubikGenome> .GenerationsCount = 50;
TGA <RubikGenome> .MutationRatio = 0.2;
TGA <RubikGenome> .WinnerRatio = 0.1;
var ga = new TGA <RubikGenome>();
ga.Evaluate = OnEvaluate;
ga.Progress = OnProgress;
ga.Execute();
best = ga.Best;
if (best.Fitness == 0)
{
SaveSolution(best);
}
}
if (best.Fitness < HighScore)
{
Moves.Clear();
for (int i = 0; i < best.MovesCount; i++)
{
var move = new TMove();
move.Decode((int)best.Genes[i]);
Moves.Add(move);
}
HighScore = best.Fitness;
}
MoveTimer.Start();
}