/**
* Two-point crossover.
* @param parent1 The first parent.
* @param parent2 The second parent.
* @return The child drawing.
*/
private CandidateSolution crossover(CandidateSolution parent1, CandidateSolution parent2)
{
Random random = new Random();
int startPosition = random.Next(GenotypeLength);
int endPosition = random.Next(GenotypeLength);
if (startPosition > endPosition)
{
int tmp = startPosition;
startPosition = endPosition;
endPosition = tmp;
}
CandidateSolution child = new CandidateSolution(fitnessCalculator, BitSize, GenotypeLength, GroupSize);
for (int i = 0; i < GenotypeLength; i++)
{
if (startPosition <= i && i <= endPosition)
{
child.Solution[i] = parent2.Solution[i];
}
else
{
child.Solution[i] = parent1.Solution[i];
}
}
return(child);
}
private void Redraw(int generation, CandidateSolution <List <DungeonOp> > bestSolution)
{
var data = new List <DungeonOp>(bestSolution.Solution);
var map = new GridMap(new Random());
foreach (var op in data)
{
op.Execute(map);
}
lock (drawLock)
{
this.Clear();
var status = $"{(DateTime.Now - this.startedOn).TotalSeconds}s | Generation {generation}, FITNESS: best={bestSolution.Fitness}, with {data.Count} ops";
this.Print(0, STATUS_Y, status, Color.White);
System.Console.WriteLine(status);
for (var y = 0; y < this.Height - 1; y++)
{
for (var x = 0; x < this.Width; x++)
{
var text = map.Get(x, y) ? "." : "#";
var colour = text == "#" ? WALL_COLOUR : FLOOR_COLOUR;
this.Print(x, y, text, colour);
}
}
}
}
public double Calculate(CandidateSolution solution)
{
double sum = 0d;
var groups = GetGroups(solution);
foreach (var group in groups.Keys)
{
var groupSum = 0;
var members = groups[group].ToArray();
for (int memberOne = 0; memberOne < members.Length; memberOne++)
{
for (int memberTwo = 0; memberTwo < members.Length; memberTwo++)
{
if (memberOne != memberTwo)
{
if (relationships[members[memberOne]].ContainsKey(members[memberTwo]))
{
groupSum++;
}
}
}
}
sum += groupSum;
}
return(sum);
}
private void PerGenerationCallback(string status, CandidateSolution <bool, ProblemState> bestThisGeneration)
{
var strategy = StrategyFactory.GetStrategyForGP(bestThisGeneration);
DisplayStrategyGrids(strategy, "");
DisplayCurrentStatus(status);
}
//-------------------------------------------------------------------------
// each candidate gets evaluated here
//-------------------------------------------------------------------------
private float EvaluateCandidate(CandidateSolution <bool, ProblemState> candidate)
{
// test every possible situation and store the candidate's suggested action in the strategy object
Strategy strategy = StrategyFactory.GetStrategyForGP(candidate);
// then test that strategy and return the total money lost/made
var strategyTester = new StrategyTester(strategy, settings.TestSettings);
return(strategyTester.GetStrategyScore(settings.TestSettings.NumHandsToPlay));
}
public static void DebugDisplayStrategy(CandidateSolution <bool, ProblemState> candidate, string prefixText)
{
string debug =
prefixText +
(String.IsNullOrWhiteSpace(prefixText) ? "" : " ") +
" Plr: " + candidate.StateData.PlayerHand +
" H: " + candidate.StateData.VotesForHit +
" S: " + candidate.StateData.VotesForStand +
" P: " + candidate.StateData.VotesForSplit +
" D: " + candidate.StateData.VotesForDoubleDown;
Debug.WriteLine(debug);
}
/**
* @return The solution with the highest fitness.
*/
public CandidateSolution getFittest()
{
CandidateSolution fittest = candidateSolutions[0];
for (int i = 1; i < candidateSolutions.Length; i++)
{
if (fittest.Fitness < candidateSolutions[i].Fitness)
{
fittest = candidateSolutions[i];
}
}
return(fittest);
}
public static Strategy GetStrategyForGP(CandidateSolution <bool, ProblemState> candidate)
{
Strategy result = new Strategy();
foreach (var upcardRank in Card.ListOfRanks)
{
if (upcardRank == Card.Ranks.Jack || upcardRank == Card.Ranks.Queen || upcardRank == Card.Ranks.King)
{
continue;
}
AddStrategyForUpcard(upcardRank, result, candidate);
}
return(result);
}
/**
* Constructs a new population.
* @param size The size of the population.
* @param initialise True, if the population will be initialised.
*/
public Population(FitnessCalculator fitnessCalculator, int size, int bitSize, int genotypeLength, int groupSize, bool initialise = false)
{
candidateSolutions = new CandidateSolution[size];
this.fitnessCalculator = fitnessCalculator;
this.groupSize = groupSize;
if (initialise)
{
for (int i = 0; i < candidateSolutions.Length; i++)
{
CandidateSolution candidateSolution = new CandidateSolution(fitnessCalculator, bitSize, genotypeLength, groupSize);
candidateSolution.GenerateIndividual();
candidateSolutions[i] = candidateSolution;
}
}
}
private Dictionary <int, List <int> > GetGroups(CandidateSolution solution)
{
var max = solution.Solution.Max();
var groups = new Dictionary <int, List <int> >();
for (int i = 1; i <= max; i++)
{
groups.Add(i, new List <int>());
}
for (int member = 0; member < solution.Solution.Length; member++)
{
groups[solution.Solution[member]].Add(member + 1);
}
return(groups);
}
static void ShowFinalResults(CandidateSolution <double, StateData> solution)
{
// then test how well it does
double finalScore;
string testDetails;
RunFinalTests(solution, out finalScore, out testDetails);
Console.WriteLine();
Console.WriteLine("Final score: " + finalScore.ToString("0.0000"));
Console.WriteLine();
Console.WriteLine(solution.ToString());
var writer = File.AppendText("test-results.csv");
writer.Write(testDetails);
writer.Close();
}
//-------------------------------------------------------------------------
// For each generation, we get information about what's going on
//-------------------------------------------------------------------------
private bool PerGenerationCallback(EngineProgress progress, CandidateSolution <bool, ProblemState> bestThisGeneration)
{
string summary =
"Upcard " + currentDealerUpcardRank +
" gen: " + progress.GenerationNumber +
" best: " + progress.BestFitnessThisGen.ToString("0") +
" avg: " + progress.AvgFitnessThisGen.ToString("0");
perGenerationCallback(summary, bestThisGeneration);
Debug.WriteLine(summary);
// keep track of how many gens we've searched
NumGenerationsNeeded++;
// return true to keep going, false to halt the system
bool keepRunning = true;
return(keepRunning);
}
/**
* Mutation of a candidate solution.
* @param candidateSolution The candidate solution.
*/
private void mutate(CandidateSolution candidateSolution)
{
Random random = new Random();
if (random.NextDouble() < MutationProbability)
{
int i = random.Next(candidateSolution.Solution.Length);
int j = random.Next(candidateSolution.Solution.Length);
while (i == j)
{
j = random.Next(candidateSolution.Solution.Length);
}
var temp = candidateSolution.Solution[i];
candidateSolution.Solution[i] = candidateSolution.Solution[j];
candidateSolution.Solution[j] = temp;
}
}
static void RunFinalTests(CandidateSolution <double, StateData> candidate, out double finalScore, out string testDetails)
{
var sampleData = new Dataset();
StringBuilder sb = new StringBuilder();
sb.AppendLine("Test results at " + DateTime.Now.ToShortDateString() + " " + DateTime.Now.ToShortTimeString());
// run through our test data and see how close the answer the genetic program
// comes up with is to the training data. Lower fitness scores are better than bigger scores
double totalDifference = 0;
while (true)
{
var row = sampleData.GetRowOfTestingData();
if (row == null)
{
break;
}
// populate vars with values from row of the training data
for (int i = 0; i < Dataset.NumColumns; i++)
{
if (i != Dataset.LabelColumnIndex)
{
candidate.SetVariableValue("v" + i, row[i]);
}
}
var result = candidate.Evaluate();
// now figure the difference between the calculated value and the training data
var actualAnswer = row[Dataset.LabelColumnIndex];
var diff = result - actualAnswer;
totalDifference += diff * diff;
Console.WriteLine("Ans: " + actualAnswer.ToString(" 0") + " AI: " + result.ToString("0.00"));
sb.AppendLine(actualAnswer.ToString("0.0") + ", " + result.ToString("0.000"));
}
totalDifference = Math.Sqrt(totalDifference);
finalScore = (float)totalDifference;
sb.AppendLine("\"Final score\", " + finalScore.ToString("0.000"));
testDetails = sb.ToString();
}
private bool IsValid(CandidateSolution solution)
{
var counter = new Dictionary <int, int>();
for (int i = 1; i <= BitSize; i++)
{
counter.Add(i, 0);
}
for (int i = 0; i < solution.Solution.Length; i++)
{
counter[solution.Solution[i]]++;
if (counter[solution.Solution[i]] > GroupSize)
{
return(false);
}
}
return(true);
}
static float EvaluateCandidate(CandidateSolution <double, StateData> candidate)
{
var sampleData = new Dataset();
// run through our test data and see how close the answer the genetic program
// comes up with is to the training data. Lower fitness scores are better than bigger scores
double totalDifference = 0;
while (true)
{
var row = sampleData.GetRowOfTrainingData();
if (row == null)
{
break;
}
// populate vars with values from row of the training data, and then get the calculated value
for (int i = 0; i < Dataset.NumColumns; i++)
{
if (i != Dataset.LabelColumnIndex)
{
candidate.SetVariableValue("v" + i, row[i]);
}
}
var result = candidate.Evaluate();
// now figure the difference between the calculated value and the training data
var actualAnswer = row[Dataset.LabelColumnIndex];
var diff = result - actualAnswer;
totalDifference += diff * diff;
}
// sqrt of the summed squared diffences
totalDifference = Math.Sqrt(totalDifference);
// fitness function returns a float
return((float)totalDifference);
}
public abstract void SetCandidateRef(CandidateSolution <T, S> candidate);
public override void SetCandidateRef(CandidateSolution <T, S> candidate)
{
// no need to store a reference to the candidate for a constant
}
public void BuildProgram(
int populationSize,
int crossoverPercentage,
double mutationPercentage,
int ElitismPercentage,
int tourneySize,
Action <string> currentStatusCallback,
string dealerUpcardRankToUse)
{
displayGenerationCallback = currentStatusCallback;
dealerUpcardRank = dealerUpcardRankToUse;
// just some values, leave the rest as defaults
var engineParams = new EngineParameters()
{
CrossoverRate = crossoverPercentage / 100F,
ElitismRate = ElitismPercentage / 100F,
IsLowerFitnessBetter = false,
MutationRate = mutationPercentage / 100F,
PopulationSize = populationSize,
TourneySize = tourneySize
};
// create the engine. each tree (and node within the tree) will return a bool.
// we also indicate the type of our problem state data (used by terminal functions and stateful functions)
var engine = new Engine <bool, ProblemState>(engineParams);
// no constants for this problem
// no variables for this solution - we can't pass in information about our hand
// via boolean variables, so we do it via some terminal functions instead
// for a boolean tree, we use the standard operators
engine.AddFunction((a, b) => a || b, "Or");
engine.AddFunction((a, b, c) => a || b || c, "Or3");
engine.AddFunction((a, b) => a && b, "And");
engine.AddFunction((a, b, c) => a && b && c, "And3");
engine.AddFunction((a) => !a, "Not");
// then add functions to indicate a strategy
engine.AddStatefulFunction(HitIf, "HitIf");
engine.AddStatefulFunction(StandIf, "StandIf");
engine.AddStatefulFunction(DoubleIf, "DoubleIf");
engine.AddStatefulFunction(SplitIf, "SplitIf");
// terminal functions to look at game state
// first, player holding ace?
engine.AddTerminalFunction(HasAce, "HasAce");
engine.AddTerminalFunction(HasPair, "HasPair");
// player hand totals
engine.AddTerminalFunction(HandVal4, "Has4");
engine.AddTerminalFunction(HandVal5, "Has5");
engine.AddTerminalFunction(HandVal6, "Has6");
engine.AddTerminalFunction(HandVal7, "Has7");
engine.AddTerminalFunction(HandVal8, "Has8");
engine.AddTerminalFunction(HandVal9, "Has9");
engine.AddTerminalFunction(HandVal10, "Has10");
engine.AddTerminalFunction(HandVal11, "Has11");
engine.AddTerminalFunction(HandVal12, "Has12");
engine.AddTerminalFunction(HandVal13, "Has13");
engine.AddTerminalFunction(HandVal14, "Has14");
engine.AddTerminalFunction(HandVal15, "Has15");
engine.AddTerminalFunction(HandVal16, "Has16");
engine.AddTerminalFunction(HandVal17, "Has17");
engine.AddTerminalFunction(HandVal18, "Has18");
engine.AddTerminalFunction(HandVal19, "Has19");
engine.AddTerminalFunction(HandVal20, "Has20");
// num cards held
engine.AddTerminalFunction(Holding2Cards, "Hold2");
engine.AddTerminalFunction(Holding3Cards, "Hold3");
// pass a fitness evaluation function and run
engine.AddFitnessFunction((t) => EvaluateCandidate(t));
// and add something so we can track the progress
engine.AddProgressFunction((t) => PerGenerationCallback(t));
BestSolution = engine.FindBestSolution();
}
//-------------------------------------------------------------------------
// each candidate gets evaluated here
//-------------------------------------------------------------------------
private float EvaluateCandidate(CandidateSolution <bool, ProblemState> candidate)
{
int playerChips = 0;
for (int handNum = 0; handNum < TestConditions.NumHandsToPlay; handNum++)
{
// for each hand, we generate a random deck. Blackjack is often played with multiple decks to improve the house edge
MultiDeck deck = new MultiDeck(TestConditions.NumDecks);
// always use the designated dealer upcard (of hearts), so we need to remove from the deck so it doesn't get used twice
deck.RemoveCard(dealerUpcardRank, "H");
Hand dealerHand = new Hand();
Hand playerHand = new Hand();
playerHand.AddCard(deck.DealCard());
dealerHand.AddCard(new Card(dealerUpcardRank, "H"));
playerHand.AddCard(deck.DealCard());
dealerHand.AddCard(deck.DealCard());
// save the cards in state, and reset the votes for this hand
candidate.StateData.PlayerHands.Clear();
candidate.StateData.PlayerHands.Add(playerHand);
// do the intial wager
int totalBetAmount = TestConditions.BetSize;
playerChips -= TestConditions.BetSize;
// outer loop is for each hand the player holds. Obviously this only happens when they've split a hand
for (int handIndex = 0; handIndex < candidate.StateData.PlayerHands.Count; handIndex++)
{
candidate.StateData.HandIndex = handIndex;
playerHand = candidate.StateData.PlayerHand; // gets the current hand, based on index
// loop until the hand is done
var currentHandState = TestConditions.GameState.PlayerDrawing;
// check for player having a blackjack, which is an instant win
if (playerHand.HandValue() == 21)
{
// if the dealer also has 21, then it's a tie
if (dealerHand.HandValue() != 21)
{
currentHandState = TestConditions.GameState.PlayerBlackjack;
playerChips += TestConditions.BlackjackPayoffSize;
}
else
{
// a tie means we just ignore it and drop through
currentHandState = TestConditions.GameState.HandComparison;
}
}
// check for dealer having blackjack, which is either instant loss or tie
if (dealerHand.HandValue() == 21)
{
currentHandState = TestConditions.GameState.HandComparison;
}
// player draws
while (currentHandState == TestConditions.GameState.PlayerDrawing)
{
// get the decision
candidate.StateData.VotesForDoubleDown = 0;
candidate.StateData.VotesForHit = 0;
candidate.StateData.VotesForStand = 0;
candidate.StateData.VotesForSplit = 0;
candidate.Evaluate(); // throw away the result, because it's meaningless
// look at the votes to see what to do
var action = GetAction(candidate.StateData);
// if there's an attempt to double-down with more than 2 cards, turn into a hit
if (action == ActionToTake.Double && playerHand.Cards.Count > 2)
{
action = ActionToTake.Hit;
}
// if we're trying to split, but don't have a pair, turn that into a stand?
if (action == ActionToTake.Split && !playerHand.IsPair())
{
Debug.Assert(false, "Vote for split without a pair");
}
switch (action)
{
case ActionToTake.Hit:
// hit me
playerHand.AddCard(deck.DealCard());
// if we're at 21, we're done
if (playerHand.HandValue() == 21)
{
currentHandState = TestConditions.GameState.DealerDrawing;
}
// did we bust?
if (playerHand.HandValue() > 21)
{
currentHandState = TestConditions.GameState.PlayerBusted;
}
break;
case ActionToTake.Stand:
// if player stands, it's the dealer's turn to draw
currentHandState = TestConditions.GameState.DealerDrawing;
break;
case ActionToTake.Double:
// double down means bet another chip, and get one and only card card
playerChips -= TestConditions.BetSize;
totalBetAmount += TestConditions.BetSize;
playerHand.AddCard(deck.DealCard());
if (playerHand.HandValue() > 21)
{
currentHandState = TestConditions.GameState.PlayerBusted;
}
else
{
currentHandState = TestConditions.GameState.DealerDrawing;
}
break;
case ActionToTake.Split:
// do the split and add the hand to our collection
var newHand = new Hand();
newHand.AddCard(playerHand.Cards[1]);
playerHand.Cards[1] = deck.DealCard();
newHand.AddCard(deck.DealCard());
candidate.StateData.PlayerHands.Add(newHand);
//Debug.WriteLine("TID " + AppDomain.GetCurrentThreadId() + " " +
// "is splitting and has " + candidate.StateData.PlayerHands.Count + " hands");
Debug.Assert(candidate.StateData.PlayerHands.Count < 5, "Too many hands for player");
// our extra bet
playerChips -= TestConditions.BetSize;
// we don't adjust totalBetAmount because each bet pays off individually, so the total is right
//totalBetAmount += TestConditions.BetSize;
break;
}
}
// if the player busted, nothing to do, since chips have already been consumed. Just go on to the next hand
// on the other hand, if the player hasn't busted, then we need to play the hand for the dealer
while (currentHandState == TestConditions.GameState.DealerDrawing)
{
// if player didn't bust or blackjack, dealer hits until they have 17+ (hits on soft 17)
if (dealerHand.HandValue() < 17)
{
dealerHand.AddCard(deck.DealCard());
if (dealerHand.HandValue() > 21)
{
currentHandState = TestConditions.GameState.DealerBusted;
playerChips += totalBetAmount * 2; // the original bet and a matching amount
}
}
else
{
// dealer hand is 17+, so we're done
currentHandState = TestConditions.GameState.HandComparison;
}
}
if (currentHandState == TestConditions.GameState.HandComparison)
{
int playerHandValue = playerHand.HandValue();
int dealerHandValue = dealerHand.HandValue();
// if it's a tie, give the player his bet back
if (playerHandValue == dealerHandValue)
{
playerChips += totalBetAmount;
}
else
{
if (playerHandValue > dealerHandValue)
{
// player won
playerChips += totalBetAmount * 2; // the original bet and a matching amount
}
else
{
// player lost, nothing to do since the chips have already been decremented
}
}
}
}
}
return(playerChips);
}
private static void AddStrategyForUpcard(Card.Ranks upcardRank, Strategy result, CandidateSolution <bool, ProblemState> candidate)
{
Card dealerCard = new Card(upcardRank, Card.Suits.Diamonds);
// do pairs
for (var pairedRank = Card.Ranks.Ace; pairedRank >= Card.Ranks.Two; pairedRank--)
{
// build player hand
Hand playerHand = new Hand();
playerHand.AddCard(new Card(pairedRank, Card.Suits.Hearts));
playerHand.AddCard(new Card(pairedRank, Card.Suits.Spades));
// find strategy
SetupStateData(candidate.StateData, playerHand, dealerCard);
candidate.Evaluate();
// get the decision and store in the strategy object
var action = GetActionFromCandidate(candidate.StateData);
result.SetActionForPair(upcardRank, pairedRank, action);
}
// then soft hands
// we don't start with Ace, because that would be AA, which is handled in the pair zone
// we also don't start with 10, since that's blackjack. So 9 is our starting point
for (int otherCard = 9; otherCard > 1; otherCard--)
{
// build player hand
Hand playerHand = new Hand();
// first card is an ace, second card is looped over
playerHand.AddCard(new Card(Card.Ranks.Ace, Card.Suits.Hearts));
playerHand.AddCard(new Card((Card.Ranks)otherCard, Card.Suits.Spades));
// find strategy
SetupStateData(candidate.StateData, playerHand, dealerCard);
candidate.Evaluate();
// get the decision and store in the strategy object
var action = GetActionFromCandidate(candidate.StateData);
result.SetActionForSoftHand(upcardRank, otherCard, action);
}
// hard hands.
for (int hardTotal = 20; hardTotal > 4; hardTotal--)
{
// build player hand
Hand playerHand = new Hand();
// divide by 2 if it's even, else add one and divide by two
int firstCardRank = ((hardTotal % 2) != 0) ? (hardTotal + 1) / 2 : hardTotal / 2;
int secondCardRank = hardTotal - firstCardRank;
// 20 is always TT, which is a pair, so we handle that by building a 3 card hand
if (hardTotal == 20)
{
playerHand.AddCard(new Card(Card.Ranks.Ten, Card.Suits.Diamonds));
firstCardRank = 6;
secondCardRank = 4;
}
// we don't want pairs, so check for that
if (firstCardRank == secondCardRank)
{
firstCardRank++;
secondCardRank--;
}
playerHand.AddCard(new Card((Card.Ranks)firstCardRank, Card.Suits.Diamonds));
playerHand.AddCard(new Card((Card.Ranks)secondCardRank, Card.Suits.Spades));
// find strategy
SetupStateData(candidate.StateData, playerHand, dealerCard);
candidate.Evaluate();
// get the decision and store in the strategy object
var action = GetActionFromCandidate(candidate.StateData);
result.SetActionForHardHand(upcardRank, hardTotal, action);
}
}
/// <summary>
/// Calculate and return the optimal solution to the given problem.
/// </summary>
/// <param name="problem">The problem whose optimal solution we seek</param>
/// <returns>The optimal solution</returns>
public Solution OptimalSoultion(Problem problem)
{
// Figure out our temperature and strength requirements
double targetTemp = 0.0;
double strengthReq = 0.0;
foreach (Constraint constraint in problem.Constraints)
{
switch (constraint.Value)
{
case VALUE.TEMP:
targetTemp = constraint.Target;
break;
case VALUE.FORCE_LIMIT:
strengthReq = constraint.Target;
break;
}
}
// Enumerate solutions for every combination of material and cooler
List <CandidateSolution> candidates = new List <CandidateSolution>();
foreach (Material m in materials)
{
foreach (Cooler c in coolers)
{
CandidateSolution cs = new CandidateSolution(m, c, targetTemp, strengthReq, problem);
if (cs.IsFeasible)
{
candidates.Add(cs);
}
}
}
// Find the best solution
double solutionCost = Double.MaxValue;
CandidateSolution optimalSolution = null;
foreach (CandidateSolution c in candidates)
{
if (c.SolutionCost < solutionCost)
{
optimalSolution = c;
solutionCost = c.SolutionCost;
}
}
Solution answer = null;
if (candidates.Count == 0)
{
return(null);
}
else
{
answer = new Solution(
problem.Name,
optimalSolution.Cooler.Name,
optimalSolution.CoolerPowerFactor,
optimalSolution.Material.Name,
optimalSolution.StrutCrossSection,
optimalSolution.StrutLength,
optimalSolution.SolutionCost
);
}
return(answer);
}
public override void BuildProgram(ProgramSettings settings, Action <string> currentStatusCallback)
{
this.settings = settings;
displayGenerationCallback = currentStatusCallback;
// create the engine. each tree (and node within the tree) will return a bool.
// we also indicate the type of our problem state data (used by terminal functions and stateful functions)
var engine = new Engine <bool, ProblemState>(settings.GPsettings);
// no constants for this problem
// no variables for this solution - we can't pass in information about our hand
// via boolean variables, so we do it via some terminal functions instead
// for a boolean tree, we use the standard operators
engine.AddFunction((a, b) => a || b, "Or");
engine.AddFunction((a, b, c) => a || b || c, "Or3");
engine.AddFunction((a, b) => a && b, "And");
engine.AddFunction((a, b, c) => a && b && c, "And3");
engine.AddFunction((a) => !a, "Not");
// then add functions to indicate a strategy
engine.AddStatefulFunction(HitIf, "HitIf");
engine.AddStatefulFunction(StandIf, "StandIf");
engine.AddStatefulFunction(DoubleIf, "DoubleIf");
engine.AddStatefulFunction(SplitIf, "SplitIf");
//----------------------------------------------
// terminal functions to look at game state
//----------------------------------------------
// soft hands
engine.AddTerminalFunction(AcePlus2, "AcePlus2");
engine.AddTerminalFunction(AcePlus3, "AcePlus3");
engine.AddTerminalFunction(AcePlus4, "AcePlus4");
engine.AddTerminalFunction(AcePlus5, "AcePlus5");
engine.AddTerminalFunction(AcePlus6, "AcePlus6");
engine.AddTerminalFunction(AcePlus7, "AcePlus7");
engine.AddTerminalFunction(AcePlus8, "AcePlus8");
engine.AddTerminalFunction(AcePlus9, "AcePlus9");
// pairs
engine.AddTerminalFunction(HasPairTwos, "HasPair2");
engine.AddTerminalFunction(HasPairThrees, "HasPair3");
engine.AddTerminalFunction(HasPairFours, "HasPair4");
engine.AddTerminalFunction(HasPairFives, "HasPair5");
engine.AddTerminalFunction(HasPairSixes, "HasPair6");
engine.AddTerminalFunction(HasPairSevens, "HasPair7");
engine.AddTerminalFunction(HasPairEights, "HasPair8");
engine.AddTerminalFunction(HasPairNines, "HasPair9");
engine.AddTerminalFunction(HasPairTens, "HasPairT");
engine.AddTerminalFunction(HasPairAces, "HasPairA");
// hard hand totals
engine.AddTerminalFunction(HandVal5, "Hard5");
engine.AddTerminalFunction(HandVal6, "Hard6");
engine.AddTerminalFunction(HandVal7, "Hard7");
engine.AddTerminalFunction(HandVal8, "Hard8");
engine.AddTerminalFunction(HandVal9, "Hard9");
engine.AddTerminalFunction(HandVal10, "Hard10");
engine.AddTerminalFunction(HandVal11, "Hard11");
engine.AddTerminalFunction(HandVal12, "Hard12");
engine.AddTerminalFunction(HandVal13, "Hard13");
engine.AddTerminalFunction(HandVal14, "Hard14");
engine.AddTerminalFunction(HandVal15, "Hard15");
engine.AddTerminalFunction(HandVal16, "Hard16");
engine.AddTerminalFunction(HandVal17, "Hard17");
engine.AddTerminalFunction(HandVal18, "Hard18");
engine.AddTerminalFunction(HandVal19, "Hard19");
engine.AddTerminalFunction(HandVal20, "Hard20");
// upcards
engine.AddTerminalFunction(DealerShows2, "Dlr2");
engine.AddTerminalFunction(DealerShows3, "Dlr3");
engine.AddTerminalFunction(DealerShows4, "Dlr4");
engine.AddTerminalFunction(DealerShows5, "Dlr5");
engine.AddTerminalFunction(DealerShows6, "Dlr6");
engine.AddTerminalFunction(DealerShows7, "Dlr7");
engine.AddTerminalFunction(DealerShows8, "Dlr8");
engine.AddTerminalFunction(DealerShows9, "Dlr9");
engine.AddTerminalFunction(DealerShows10, "Dlr10");
engine.AddTerminalFunction(DealerShowsA, "DlrA");
// pass a fitness evaluation function and run
engine.AddFitnessFunction((t) => EvaluateCandidate(t));
// and add something so we can track the progress
engine.AddProgressFunction((t) => PerGenerationCallback(t));
Solution = engine.FindBestSolution();
Fitness = Solution.Fitness;
FinalStatus = "Final Score: " + Fitness.ToString() + "\n" +
"Generations needed to find: " + NumGenerationsNeeded;
}
public override void SetCandidateRef(CandidateSolution <T, S> candidate)
{
ownerCandidate = candidate;
}
public VariableNode(string varName, CandidateSolution <T, S> candidate)
{
variableName = varName;
ownerCandidate = candidate;
}
/**
* Evolve the population.
* @param pop The population.
* @return The evolved population.
*/
private Population EvolvePopulation(Population population)
{
Array.Sort(population.CandidateSolutions, delegate(CandidateSolution x, CandidateSolution y)
{
if (x.Fitness > y.Fitness)
{
return(-1);
}
else if (x.Fitness < y.Fitness)
{
return(1);
}
return(0);
});
Population matingPool = GenerateMatingPool(population);
Population offsetSpringPopulation = new Population(fitnessCalculator, PopulationSize, BitSize, GenotypeLength, GroupSize);
// Crossover operation.
Random random = new Random();
for (int i = 0; i < offsetSpringPopulation.CandidateSolutions.Length; i++)
{
CandidateSolution child = null;
var probability = random.NextDouble();
if (CrossoverProbability < probability)
{
child = matingPool.CandidateSolutions[i];
}
else
{
CandidateSolution parent1 = matingPool.CandidateSolutions[i];
bool reject = true;
int trial = 0;
do
{
CandidateSolution parent2 = selection(matingPool);
child = crossover(parent1, parent2);
if (IsValid(child))
{
reject = false;
}
else
{
trial++;
}
}while (reject && trial < 10);
if (trial == 10)
{
child = parent1;
}
}
offsetSpringPopulation.CandidateSolutions[i] = child;
}
// Mutation.
for (int i = 0; i < offsetSpringPopulation.CandidateSolutions.Length; i++)
{
mutate(offsetSpringPopulation.CandidateSolutions[i]);
}
// Survivor selection
Array.Sort(offsetSpringPopulation.CandidateSolutions, delegate(CandidateSolution x, CandidateSolution y)
{
if (x.Fitness > y.Fitness)
{
return(-1);
}
else if (x.Fitness < y.Fitness)
{
return(1);
}
return(0);
});
Population newPopulation = new Population(fitnessCalculator, PopulationSize, BitSize, GenotypeLength, GroupSize);
var parentCount = (int)(population.CandidateSolutions.Length * SurvivalProbability);
var offsetSpringCount = (int)(offsetSpringPopulation.CandidateSolutions.Length * (1.0d - SurvivalProbability));
while (parentCount + offsetSpringCount < PopulationSize)
{
if (parentCount % 2 == 1)
{
parentCount++;
}
else
{
offsetSpringCount++;
}
}
int counter = 0;
for (int i = 0; i < newPopulation.CandidateSolutions.Length; i++)
{
if (i < parentCount)
{
newPopulation.CandidateSolutions[i] = population.CandidateSolutions[i];
}
else
{
newPopulation.CandidateSolutions[i] = offsetSpringPopulation.CandidateSolutions[counter];
counter++;
}
}
return(newPopulation);
}
