public float CombinedFitness()
{
float fitA = 1 - Result.HpPercentage;
float fitB = (float)PlayoutResult.LengthSample(Result.TotalTurns);
float fitC = Team1.DistanceFitness(Team2);
float fitness = (fitA + fitB + fitC) / 3;
if (!Result.AllPlayed)
{
fitness = 0.0001f;
}
return(fitness);
}
public void RunEvolutionStrategies(DNA initialDna, bool evolveTeam1 = true, int?forcedIndex = null,
bool stopWhenGood = false)
{
if (!_keepCounter)
{
GoodCount = 0;
}
var t1 = initialDna;
var t2 = t1.Clone();
if (evolveTeam1)
{
t1.Randomize();
}
t2.Randomize();
var map = Map.Load("data/map.json");
Console.WriteLine($"Initial ({t1.Data.Count}): {t1.ToDnaString()}\n\n");
var game = GameSetup.GenerateFromDna(t1, t2, map);
int restartCount = 0;
var current = new Individual(t1.Clone(),
t2.Clone(),
EvolutionBenchmark.CalculateFitness(game, t1, t2));
List <double> plotT = new List <double>();
List <double> plotFit = new List <double>();
List <double> plotHpPercentage = new List <double>();
List <double> plotLength = new List <double>();
List <double> plotDistance = new List <double>();
var gameCopies = Enumerable.Range(0, Constants.TeamsPerGeneration)
#if PARALLEL
.AsParallel()
#endif
.Select(_ => game.DeepCopy())
.ToList();
int i;
for (i = 0; i < Constants.NumGenerations; i++)
{
var genWatch = new Stopwatch();
genWatch.Start();
var teamWatch = new Stopwatch();
teamWatch.Start();
if (Constants.RestartFailures && current.CombinedFitness() < Constants.FitnessThreshold)
{
if (evolveTeam1)
{
current.Team1.Randomize();
}
current.Team2.Randomize();
restartCount++;
}
var generation = Enumerable.Range(0, Constants.TeamsPerGeneration)
#if PARALLEL
.AsParallel()
#endif
.Select(j => {
var newTeam1 =
evolveTeam1
? EvolutionBenchmark.Mutate(current.Team1)
: current.Team1;
var newTeam2 = EvolutionBenchmark.Mutate(current.Team2);
var newFitness =
EvolutionBenchmark.CalculateFitness(
gameCopies[j], newTeam1, newTeam2);
return(new Individual(newTeam1, newTeam2, newFitness));
})
.ToList();
var previous = current;
current = EvolutionStrategy(game, previous, generation, evolveTeam1);
plotT.Add(i);
plotFit.Add(current.CombinedFitness());
plotHpPercentage.Add(1 - current.Result.HpPercentage);
plotLength.Add(PlayoutResult.LengthSample(current.Result.TotalTurns));
plotDistance.Add(current.Team1.DistanceFitness(current.Team2));
if (i % Constants.EvolutionPrintModulo == 0)
{
Console.WriteLine($"T: {i}\t\t" +
$"F: {previous.CombinedFitness().ToString("0.0000")}" +
$" -> {current.CombinedFitness().ToString("0.0000")}");
}
if (Constants.SaveGoodOnes && current.CombinedFitness() > 0.95)
{
int index = forcedIndex ?? GoodCount + GoodIndexOffset;
EvolutionBenchmark.SaveDna(index, current.Team1, current.Team2);
GoodCount++;
Console.WriteLine($"Found extra good {current.CombinedFitness()}, restarting");
if (evolveTeam1)
{
current.Team1.Randomize();
}
current.Team2.Randomize();
if (stopWhenGood || GoodCount >= _maxGoodCount)
{
Console.WriteLine($"Stopping evolution early, reached target {_maxGoodCount} good matches.");
break;
}
}
}
Console.WriteLine($"Restarts: {restartCount}");
if (Constants.GnuPlot)
{
GnuPlot.HoldOn();
GnuPlot.Set($"xrange [0:{i}] reverse",
$"title '{Constants.NumGenerations} generations",
"key tmargin center horizontal");
GnuPlot.Plot(plotT.ToArray(), plotFit.ToArray(), $"title 'Fitness {Constants.NumGenerations}gen'");
GnuPlot.Plot(plotT.ToArray(), plotHpPercentage.ToArray(), $"title 'HP percentage'");
GnuPlot.Plot(plotT.ToArray(), plotLength.ToArray(), "title 'Game length'");
GnuPlot.Plot(plotT.ToArray(), plotDistance.ToArray(), "title 'Team difference'");
Console.ReadKey();
}
}
public Individual(DNA team1, DNA team2, PlayoutResult result)
{
Team1 = team1;
Team2 = team2;
Result = result;
}