public int Fitness(Maze maze, ChromosomeEntity ch)
{
bool isFound = false;
//iterate over chromosome's movement array
foreach (int move in ch.Chromosome)
{
switch (move)
{
case 0:
break;
case 1:
maze.CurrentPlayer.MoveUp();
isFound = maze.UpdateMaze();
break;
case 2:
maze.CurrentPlayer.MoveDown();
isFound = maze.UpdateMaze();
break;
case 3:
maze.CurrentPlayer.MoveLeft();
isFound = maze.UpdateMaze();
break;
case 4:
maze.CurrentPlayer.MoveRight();
isFound = maze.UpdateMaze();
break;
default:
break;
}
if (isFound)
{
break;
}
}
if (!isFound)
{
ch.Score = AppHelper.ManDistance(maze) + maze.CurrentPlayer.Penalties * 100;
}
else
{
ch.Score = -1;
}
return(ch.Score);
}
public ChromosomeEntity Mutation(ChromosomeEntity ch)
{
Random rnd = new Random();
if (MutationRate > rnd.NextDouble())
{
//ch.Chromosome[rnd.Next(MaxMove)] = rnd.Next(1, 5); //single gene mutation
int genescount = new Random().Next(5); //to mutate genes count
for (int i = 0; i < genescount; i++)
{
ch.Chromosome[rnd.Next(MaxMove)] = rnd.Next(1, 5); //new genes value
}
}
return(ch);
}
public void CreatePopulation()
{
Random rnd = new Random();
//Generation population
for (int pop = 0; pop < PopulationSize; pop++)
{
ChromosomeEntity ch = new ChromosomeEntity();
ch.Chromosome = new int[MaxMove];
for (int i = 0; i < MaxMove; i++)
{
ch.Chromosome[i] = rnd.Next(1, 5);
}
Population.Add(ch);
}
}
public List <ChromosomeEntity> Crossover(List <ChromosomeEntity> FitPopulation, Maze defaultMaze)
{
Random rnd = new Random();
List <ChromosomeEntity> newPopulation = new List <ChromosomeEntity>(); //for next generation
for (int i = 0; i < PopulationSize; i++)
{
ChromosomeEntity parent = FitPopulation[rnd.Next(FitPopulation.Count)]; //find a parent from prev generation
ChromosomeEntity parent2 = FitPopulation[rnd.Next(FitPopulation.Count)]; //find other parent
int crossoverIndex = rnd.Next(MaxMove);
ChromosomeEntity child = new ChromosomeEntity();
var newCh = new List <int>();
newCh.AddRange(parent.Chromosome.Take(crossoverIndex)); //taking genes from parent1
newCh.AddRange(parent2.Chromosome.Skip(crossoverIndex).Take(MaxMove - crossoverIndex)); //genes from parent2
child.Chromosome = newCh.ToArray();
child = Mutation(child); //Mutation func for new child
//to make better new generation if child has worst score keep adding parents to new generation
int p1 = Fitness(defaultMaze.DeepCopy(), parent);
int p2 = Fitness(defaultMaze.DeepCopy(), parent2);
int c1 = Fitness(defaultMaze.DeepCopy(), child);
if (c1 != -1 || p1 != -1 || p2 != -1)
{
if (p1 <= c1 && p1 <= p2)
{
newPopulation.Add(parent);
}
else if (c1 < p2)
{
newPopulation.Add(child);
}
else
{
newPopulation.Add(parent2);
}
}
}
return(newPopulation);
}
