/// <summary>
/// Does the a basic sudo genetic algorithm to determine the most optimal values for KingsWorth, PawnsWorth, KingsDangerValue, PawnsDangerValue.
/// This was not originally part of the project. This was my attempt at letting the AI tell me what works best for these values.
/// <para>
/// This method essentially works by using a WinningGenome and RandomGenome. The starting winning genome is based on the initial settings file.
/// The random genome is based on the current winning genome with slighly increased or decreased values. The algorithm runs through n simulations
/// and tracks the number of wins the random genome gets. If the random genome wins more than 50% of the time, it becomes the new winning genome.
/// The new randome genome is based on that winner and the loop starts over.
/// </para>
/// </summary>
private void DoGeneticAlgo()
{
GeneticProgress currentProgress = GeneticProgress.GetGeneticProgressInstance();
WinningGenome winningGenome = WinningGenome.GetWinningGenomeInstance();
RandomGenome randomGenome = RandomGenome.GetRandomGenomeInstance();
while (currentProgress.NumberOfGames < Settings.NumberOfSimulations)
{
try
{
RunAIDuel();
}
catch (AIException ex)
{
//Dont count this game
Logger.Error("AI Exception was caught: " + ex.Message);
//reset game
InitializeCheckers();
continue;
}
currentProgress.NumberOfGames++;
object winner = checkerBoard.GetWinner();
if (winner != null && winner is PlayerColor winningPlayer && winningPlayer == PlayerColor.Red)
{
currentProgress.NumberOfRandomGenomeWins++;
}
Logger.Info("AI Game Finished, Winner was " + winner);
Logger.Info(string.Format(
"Current Stats -- NumberOfGamesPlayed: {0}, NumberOfRandomGenomeWins {1}",
currentProgress.NumberOfGames,
currentProgress.NumberOfRandomGenomeWins));
//reset game
InitializeCheckers();
}
//Simulations on this genetic variation finished. If more losses than wins, the new random genome was better
if (currentProgress.NumberOfRandomGenomeWins > currentProgress.NumberOfGames / 2)
{
//Random Genome won more than half the games. This means we have a new winner
winningGenome.SetNewWinningGenome(randomGenome);
}
//mutate new genome
randomGenome.MutateGenomeAndSave();
//reset progress
currentProgress.ResetValues();
currentProgress.NumberOfRounds++;
}
/// <summary>
/// Main loop for the basic genetic algorithm
/// </summary>
private void GeneticAlgoLoop()
{
int roundNumber = 0;
while (roundNumber < Settings.NumberOfRounds)
{
DoGeneticAlgo();
Logger.Info("Finished Round Number: " + roundNumber++);
}
//finish up and delete temp file
GeneticProgress.GetGeneticProgressInstance().Delete();
}