// 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);
}
/// <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
