public static void Crossover(DecisionTree individual1, DecisionTree individual2, CryptoRandom r)
{
//wybranie punktu przeciecia z zakresu elementCount
//dla obu osobnikow
//zamiana miejscami tylko poddrzew wybranych elementow
int locus1;
int locus2;
Node node1;
Node node2;
do
{
locus1 = r.Next(1, individual1.elementCount + 1);
node1 = individual1.Find(locus1, individual1.root);
} while (node1 is ResultNode);
do
{
locus2 = r.Next(1, individual2.elementCount + 1);
node2 = individual2.Find(locus2, individual2.root);
} while (node2 is ResultNode);
Node tempNode = null;
tempNode = node1.leftChild;
node1.leftChild = node2.leftChild;
node2.leftChild = tempNode;
tempNode = null;
tempNode = node1.rightChild;
node1.rightChild = node2.rightChild;
node2.rightChild = tempNode;
individual1.RecalculateKeys();
individual2.RecalculateKeys();
}
public static void CrossoverWithParent(DecisionTree individual1, DecisionTree individual2, CryptoRandom r)
{
int locus1;
int locus2;
Node node1;
Node node2;
Node parent1;
Node parent2;
locus1 = r.Next(2, individual1.elementCount + 1);
node1 = individual1.Find(locus1, individual1.root);
parent1 = individual1.FindParentOfNode(locus1, individual1.root);
locus2 = r.Next(2, individual2.elementCount + 1);
node2 = individual2.Find(locus2, individual2.root);
parent2 = individual2.FindParentOfNode(locus2, individual2.root);
if (parent1.leftChild.Equals(node1))
{
parent1.leftChild = node2;
}
else
{
parent1.rightChild = node2;
}
if (parent2.leftChild.Equals(node2))
{
parent2.leftChild = node1;
}
else
{
parent2.rightChild = node1;
}
individual1.RecalculateKeys();
individual2.RecalculateKeys();
}
static void Main(string[] args)
{
string line = "";
//State state = StateInit();
State state = new State();
StateInit(state);
State maxValues = MaxValuesInit();
List <Test> tests = TestsInit();
int iterations = 100;
int populationSize = 10;
double crossoverProb = 0.1;
double mutationProb = 0.1;
double averageFitness = 0;
CryptoRandom r = new CryptoRandom();
List <DecisionTree> population = new List <DecisionTree>();
DecisionTree enemy = new DecisionTree(new List <int> {
100, 100, 50, 0
}, state, maxValues, tests, r);
//DecisionTree enemy = CreateEnemy(r, state, tests);
//population init
for (int i = 0; i < populationSize; i++)
{
population.Add(new DecisionTree(new List <int> {
100, 100, 50, 0
}, state, maxValues, tests, r));
//Console.WriteLine("Wielkosc drzewa: {0}", population[i].elementCount);
//Console.WriteLine("Wielkosc drzewa': {0}", population[i].CountElements());
}
while (iterations > 0) //glowna petla programu, ustalona ilosc iteracji, pozniej dodac zatrzymanie po stagnacji
{
for (int j = 0; j < populationSize; j++)
{
//petla walki
int turn = 1;
while (turn <= 99 && state["HP"] > 0 && state["enemyHP"] > 0)
{
//Console.WriteLine("Osobnik {0}, tura: {1}", j, turn);
//EnemyMakeMove(enemy.decide(state), state);
EnemyMoves(state);
MakeMove(population[j].decide(state), state);
turn++;
}
//Console.WriteLine("HP -> {0}", state["HP"]);
//Console.WriteLine("enemyHP -> {0}", state["enemyHP"]);
//Console.WriteLine("distance -> {0}", state["distance"]);
//Console.WriteLine("isInDanger -> {0}", state["isInDanger"]);
//Console.WriteLine("enemyIsInDanger -> {0}", state["enemyIsInDanger"]);
//Console.WriteLine("isDefending -> {0}", state["isDefending"]);
//Console.WriteLine("enemyIsDefending -> {0}", state["enemyIsDefending"]);
//Console.ReadLine();
population[j].fitness = Evaluate(state, maxValues, turn, population[j].elementCount);
//state = StateInit();
StateInit(state);
}
averageFitness = population.Sum(p => p.fitness) / populationSize;
Console.WriteLine("srednia wartosc funkcji oceny: " + averageFitness);
Console.WriteLine("najgorszy osobnik: " + population.Min(p => p.fitness));
Console.WriteLine("najlepszy osobnik: " + population.Max(p => p.fitness));
line = String.Concat(line, (averageFitness.ToString() + " " + population.Max(p => p.fitness) + " " + "\r\n"));
//selekcja
List <DecisionTree> SelectedIndividuals = EliteRankingSelection(population, populationSize, r);
//wybranie najlepszego z poprzedniej populacji i ustawienie na enemy
//enemy = population.OrderByDescending(p => p.fitness).First().Clone();
population.Clear(); // usuniecie starej populacji
//wybranie osobnikow do krzyzowania, przepisanie reszty do nowej populacji
List <DecisionTree> IndividualsToCross = new List <DecisionTree>();
for (int i = 0; i < populationSize; i++)
{
if (r.NextDouble() <= crossoverProb)
{
IndividualsToCross.Add(SelectedIndividuals[i]);
}
else
{
population.Add(SelectedIndividuals[i]);
}
}
//zapewnienie parzystej ilosci osobnikow do krzyzowania
if (IndividualsToCross.Count % 2 != 0)
{
population.Add(IndividualsToCross.Last());
IndividualsToCross.RemoveAt(IndividualsToCross.Count - 1);
}
Console.WriteLine("Skrzyzowane osobniki: " + IndividualsToCross.Count);
//krzyzowanie
while (IndividualsToCross.Count > 0)
{
DecisionTree Individual1 = IndividualsToCross[r.Next(0, IndividualsToCross.Count)];
IndividualsToCross.Remove(Individual1);
DecisionTree Individual2 = IndividualsToCross[r.Next(0, IndividualsToCross.Count)];
IndividualsToCross.Remove(Individual2);
Crossover(Individual1, Individual2, r);
population.Add(Individual1);
population.Add(Individual2);
}
int mutationCount = 0;
//mutacja
for (int i = 0; i < populationSize; i++)
{
if (r.NextDouble() <= mutationProb)
{
Mutation(population[i], r, state, maxValues, tests);
mutationCount++;
}
}
Console.WriteLine("Zmutowane osobniki: " + mutationCount);
iterations--;
Console.ReadLine();
}
System.IO.StreamWriter file = new System.IO.StreamWriter("d:\\test.txt");
file.WriteLine(line);
file.Close();
Console.ReadLine();
}
