// private List<Organism> Hive;
// private OrganismBase TheBest;
/// <summary>
/// Attempts find an optimal solution to a sudoku puzzle with the a hive of organisms
/// The organisms each have a total number of allowed Epochs (generations) to find the optimal solution.
/// If after the max epochs is reached without an optimal solution, they are culled and another hive is started within the maximum number of specified extinctions
/// NOTE: Combinatorial evolution doesn't guarantee and optimal solution will be found.
/// </summary>
/// <param name="problem">int[][]</param>
/// <param name="numOrganisms">how many virtual organism are in the hive at a given time, each representing a possible solution</param>
/// <param name="maxEpochs">number of iterations (generations) each organism can run through to find the optimal solution</param>
/// <param name="maxExtinctions">if an optimal solution isn't found after the maxEpochs, all organisms are killed and the cycle is restarted if not more than maxExtinctions.</param>
/// <returns>The best solution (int[][]) found within the specified time.</returns>
public async Task <int[][]> SolveB4ExtinctCount_Async(int[][] problem, int numOrganisms, int maxEpochs, int maxExtinctions)
{
OriginalMatrix = SudokuTool.DuplicateMatrix(problem);
// wrapper for SolveEvo()
var err = int.MaxValue;
var seed = 0;
int[][] bestSolution = null;
var extinctions = 0;
int epochs = 0;
// generate a new hive of organisms with a number of epochs to try for an error-free solution or until we've killed them off too often
while (err != 0 && extinctions < maxExtinctions)
{
_rnd = new Random(seed);
bestSolution = await SolveAsync(problem, numOrganisms, maxEpochs).ConfigureAwait(true);
err = SudokuTool.Errors(bestSolution);
extinctions++; // keep track of the attempts
Debug.WriteLine($"{err} errors prior to {extinctions} extinction level events");
++seed; // increment the seed
}
_stats.extinctions = extinctions;
return(bestSolution);
}
} // Error
/// <summary>
/// This creates a new random solution and maps that solution onto provided problem
/// </summary>
/// <param name="problem">int[][]</param>
/// <param name="rand">pass in your random object if you got one</param>
/// <returns></returns>
public static int[][] GetRandomMatrix(int[][] problem, Random rand = null)
{
Random rnd = rand ?? new Random();
// make a copy of the current puzzle
var newRandomMatrix = SudokuTool.DuplicateMatrix(problem);
for (var block = 0; block < Constants.BoardSize; ++block)
{
// fill them with 1 through 9
var corners = SudokuTool.Corner(block);
var vals = new List <int>(Constants.BoardSize);
for (var i = 1; i <= Constants.BoardSize; ++i)
{
vals.Add(i);
}
// shuffle each cell with a value from another cell
for (var k = 0; k < vals.Count; ++k)
{
var ri = rnd.Next(k, vals.Count);
var tmp = vals[k];
vals[k] = vals[ri];
vals[ri] = tmp;
}
// remove the random cell values from the known cell values
var r = corners[0];
var c = corners[1];
for (var i = r; i < r + 3; ++i)
{
for (var j = c; j < c + 3; ++j)
{
var v = problem[i][j];
if (v != 0) // a fixed starting number is assumed correct
{
vals.Remove(v);
}
}
}
// walk thru block and add the new values to the random solution output
var ptr = 0; // pointer into List
for (var i = r; i < r + 3; ++i)
{
for (var j = c; j < c + 3; ++j)
{
if (newRandomMatrix[i][j] == 0) // not occupied
{
var v = vals[ptr]; // get value from List
newRandomMatrix[i][j] = v;
++ptr; // move to next value in List
}
}
}
} // each block, k
return(newRandomMatrix);
} // RandomMatrix
} // RandomMatrix
/// <summary>
/// Randomly swap the values in two cells of a random block of the supplied matrix
/// </summary>
/// <param name="problem">the original puzzle </param>
/// <param name="matrix">the current working solution</param>
/// <param name="rand">pass in your random object if you got one</param>
/// <returns></returns>
public static int[][] EvolveMatrixAsync(int[][] problem, int[][] matrix, Random rand = null)
{
Random rnd = rand ?? new Random();
// pick a random 3x3 block,
// pick two random cells in block
// swap values
var result = SudokuTool.DuplicateMatrix(matrix);
var block = rnd.Next(0, 9); // [0,8]
//Console.WriteLine("block = " + block);
var corners = SudokuTool.Corner(block);
//Console.WriteLine("corners = " + corners[0] + " " + corners[1]);
// look for at minimum 2 cells in the neighbor matrix that might be useful in the problem
var cells = new List <int[]>();
for (var i = corners[0]; i < corners[0] + 3; ++i)
{
for (var j = corners[1]; j < corners[1] + 3; ++j)
{
//Console.WriteLine("problem " + i + " " + j + " = " + problem[i][j]);
if (problem[i][j] == 0) // a non-fixed value
{
cells.Add(new[] { i, j });
}
}
}
if (cells.Count < 2)
{
// _sb.AppendLine($"Found only {cells.Count} useful cell in the neighbor.");
throw new Exception("block " + block + " doesn't have two values to swap!");
}
// pick two. suppose there are 4 possible cells 0,1,2,3
var k1 = rnd.Next(0, cells.Count); // 0,1,2,3
var inc = rnd.Next(1, cells.Count); // 1,2,3
var k2 = (k1 + inc) % cells.Count;
//Console.WriteLine("k1 k2 = " + k1 + " " + k2);
var r1 = cells[k1][0];
var c1 = cells[k1][1];
var r2 = cells[k2][0];
var c2 = cells[k2][1];
//Console.WriteLine("r1 c1 = " + r1 + " " + c1);
//Console.WriteLine("r2 c2 = " + r2 + " " + c2);
var tmp = result[r1][c1];
result[r1][c1] = result[r2][c2];
result[r2][c2] = tmp;
return(result);
} // NeighborMatrix
} // NeighborMatrix
/// <summary>
/// for each block (3x3) in matrix2, there's a 50% chance to be added into matrix1
/// </summary>
/// <param name="matrix1">mating partner 1</param>
/// <param name="matrix2">mating partner 2</param>
/// <param name="chance">0.50 or some percential double value</param>
/// <param name="rand">pass in your random object if you got one</param>
/// <returns>a single matrix with some relationships between both provided</returns>
public static int[][] MatingResult(int[][] matrix1, int[][] matrix2, double chance = 0.50, Random rand = null)
{
Random rnd = rand ?? new Random();
var result = SudokuTool.DuplicateMatrix(matrix1);
for (var block = 0; block < 9; ++block)
{
if (rnd.NextDouble() < chance)
{
var corners = SudokuTool.Corner(block);
for (var i = corners[0]; i < corners[0] + 3; ++i)
{
for (var j = corners[1]; j < corners[1] + 3; ++j)
{
result[i][j] = matrix2[i][j];
}
}
}
}
return(result);// Task.FromResult(result);
}
/// <summary>
/// Core engine for finding a solution. Running this is going to spawn a hive of organisms (some percentage of workers, the rest explorers)
///
/// </summary>
/// <param name="problem"></param>
/// <param name="numOrganisms"></param>
/// <param name="maxEpochs"></param>
/// <returns></returns>
private async Task <int[][]> SolveAsync(int[][] problem, int numOrganisms, int maxEpochs)
{
// start fresh
var Hive = new List <Organism>(new Organism[numOrganisms]);
// the best organisms in each epoch will be stored here
var TheBest = new OrganismBase(0, null, Int32.MaxValue, 0);
// initialize combinatorial Organisms with 90% of them workers and the rest explorers
// Explorers always get a new random matrix to test against.
var numWorker = (int)(numOrganisms * 0.90);
var numExplorer = numOrganisms - numWorker;
// fill the hive with new organisms having a random puzzle and known error count and also if it was the best yet
for (var i = 0; i < numOrganisms; ++i)
{
var orgType = i < numWorker ? OrganismTypes.Worker : OrganismTypes.Explorer;
// get a new random puzzle and count the errors
int[][] rndMatrix = await Task.FromResult(SudokuTool.GetRandomMatrix(problem, _rnd));
var currentErrors = await Task.FromResult(SudokuTool.Errors(rndMatrix));
// create a new organism, give it the random puzzle and errors
Hive[i] = new Organism(orgType, rndMatrix, currentErrors, 0);
_stats.organismsBorn++;
// track the best so far puzzle and copy it to TheBest
if (currentErrors < TheBest.Error)
{
TheBest.Error = currentErrors;
TheBest.Matrix = await Task.FromResult(SudokuTool.DuplicateMatrix(rndMatrix));
_stats.randomWasTheBest++; // this should average out at some point?
// Splash();
}
}
// main loop - start the generations (epochs)
var epoch = 0;
while (epoch < maxEpochs)
{
// keep a little log for every 1000 generations
if (epoch % 1000 == 0)
{
Debug.WriteLine("epoch = " + epoch);
Debug.WriteLine("best error = " + TheBest.Error);
}
// if we somehow have no errors (optimal solution!) then just stop now.
if (TheBest.Error == 0) // solution found
{
break;
}
// process each organism and give explorers random solutions and evolve workers
// (with for loop)...why not foreach? because if it ages out we want to put a new organism in place
for (var i = 0; i < numOrganisms; ++i)
{
// we treat Explorers differently than Workers. Explorers always get a new random matrix to test against. Workers evolve, mutate, age +/- or die of old age
switch (Hive[i].Type)
{
case OrganismTypes.Explorer:
// give it a new random matrix
int[][] rndMatrix = await Task.FromResult(SudokuTool.GetRandomMatrix(problem, _rnd));
Hive[i].Matrix = await Task.FromResult(SudokuTool.DuplicateMatrix(rndMatrix));
Hive[i].Error = await Task.FromResult(SudokuTool.Errors(rndMatrix));
if (Hive[i].Error < TheBest.Error)
{
TheBest.Error = Hive[i].Error;
TheBest.Matrix = SudokuTool.DuplicateMatrix(rndMatrix);
_stats.randomWasTheBest++;
// Splash();
}
break;
case OrganismTypes.Worker:
// mutate the matrix, pick a random block and pick two random cells and swap them.
int[][] evolving = await Task.FromResult(SudokuTool.EvolveMatrixAsync(problem, Hive[i].Matrix, _rnd));
var evolutionErrors = await Task.FromResult(SudokuTool.Errors(evolving));
var probability = _rnd.NextDouble();
var rareMutation = probability < 0.001;
bool evoWorked = evolutionErrors < Hive[i].Error;
// if the evolution is better (natural selection) or if this is a rareMutation, take it
if (evoWorked || rareMutation) // better, or a mistake
{
// positive result to evolution provides refreshed genepool and resets age
if (evoWorked)
{
Hive[i].Age = 0;
_stats.evolutionWorked++;
}
// if you didn't get the better end of the deal
if (rareMutation && !evoWorked)
{
_stats.mutationFailed++;
}
Hive[i].Matrix = await Task.FromResult(SudokuTool.DuplicateMatrix(evolving)); // by value
Hive[i].Error = evolutionErrors;
if (Hive[i].Error < TheBest.Error)
{
TheBest.Error = Hive[i].Error;
TheBest.Matrix = await Task.FromResult(SudokuTool.DuplicateMatrix(evolving));
_stats.bestMutationsEver++;
// Splash();
}
}
else // evolved code is not better and there was no chance for forced mutation
{
// age this organism
Hive[i].Age++;
if (Hive[i].Age > 1000) // die - if so, get a random to replace
{
_stats.diedOfOldAge++;
// create a new replacement for the hive.
var m = await Task.FromResult(SudokuTool.GetRandomMatrix(problem, _rnd));
var me = await Task.FromResult(SudokuTool.Errors(m));
Hive[i] = new Organism(0, m, me, 0);
_stats.organismsBorn++;
}
}
break;
}
} // each organism
// find the index of the best worker
var bestWkrIndex = await Task.FromResult(SudokuTool.GetBestWorkerIndex(Hive, numWorker));
var bestWorker = Hive[bestWkrIndex];
// find the index of the best explorer
var bestExpIndex = await Task.FromResult(SudokuTool.GetBestExplorerIndex(Hive, numWorker));
var bestExplorer = Hive[bestExpIndex];
// find the index of the worst worker
var worstWkrIndex = await Task.FromResult(SudokuTool.GetWorstWorkerIndex(Hive, numWorker));
// have a 50/50 chance for each block in 2nd organism will be blended into 1st organism
var babyOrg = await Task.FromResult(SudokuTool.MatingResult(bestWorker.Matrix, bestExplorer.Matrix, 0.50, _rnd));
_stats.matingPairs++;
var babyErr = await Task.FromResult(SudokuTool.Errors(babyOrg));
var genNext = new Organism(OrganismTypes.Worker, babyOrg, babyErr, 0)
{
// generations are only incremented if one of the parents have a GeneMarker
Generation = incrementGeneration(bestWorker, bestExplorer),
// GeneMarker = "GenX" // might use this if you happened to want to see details later.
}; _stats.organismsBorn++;
// replace the worst worker with the next generation of worker
Hive[worstWkrIndex] = genNext;
_stats.culledCount++;
if (babyErr < TheBest.Error)
{
TheBest.Error = babyErr;
TheBest.Matrix = await Task.FromResult(SudokuTool.DuplicateMatrix(babyOrg));
_stats.bestBabies++;
// Splash();
}
++epoch;
_stats.epochsCompleted++;
} // while
return(TheBest.Matrix);
} // SolveEvo
