public MAP_COLOR_ARRAY Reproduce(MAP_COLOR_ARRAY[] Population, int x, int y)//CrossOver Operation in Genetic Algorithm
{
MAP_COLOR_ARRAY Result;
//Select Real X and Y according to their name x and y
MAP_COLOR_ARRAY X_MAP_COLOR = new MAP_COLOR_ARRAY(Node_Quantity);
MAP_COLOR_ARRAY Y_MAP_COLOR = new MAP_COLOR_ARRAY(Node_Quantity);
for (int i = 0; i < Node_Quantity; i++)
{
X_MAP_COLOR.Nodes_Colors[i] = Population[x].Nodes_Colors[i];
Y_MAP_COLOR.Nodes_Colors[i] = Population[y].Nodes_Colors[i];
}
int c = f.RandomRange(0, Node_Quantity - 1);
//CrossOver compound two Population
for (int i = c; i < Node_Quantity; i++)
{
X_MAP_COLOR.Nodes_Colors[i] = Y_MAP_COLOR.Nodes_Colors[i];
}
Result = X_MAP_COLOR;
return(Result);
}
public MAP_COLOR_ARRAY Mutation(MAP_COLOR_ARRAY Child) //Mutation Operation in Genetic Algorithm
{
MAP_COLOR_ARRAY Result;
int Chance = f.RandomRange(0, 20);
if (Chance > 1)
{
return(Child);
}
int Max_Change = f.RandomRange(0, Node_Quantity - 1);
for (int i = 0; i < Max_Change; i++)
{
int Rand_Num = f.RandomRange(0, Node_Quantity - 1);
int Rand_Color_Num = f.RandomRange(0, Color_Num - 1);
Child.Nodes_Colors[Rand_Num] = Rand_Color_Num;
}
Result = Child;
return(Result);
}
public void G_Algorithm(List <Edge> _edges, int _node_Quantitym, int _color_Num, int _population_Size)
{
Population_Size = _population_Size;
Node_Quantity = _node_Quantitym;
Color_Num = _color_Num;
MAP = new Graph(Node_Quantity);
_result = new int[Node_Quantity];
_result[0] = 0;
for (int u = 1; u < Node_Quantity; u++)
{
_result[u] = -1;
}
for (int i = Node_Quantity - 1; i >= 0; i--)
{
MAP.Nodes_Array[i].color = 0;
MAP.Nodes_Array[i].name = i;
}
foreach (Edge _edge in _edges)
{
int v = _edge.FirstNode.Mark;
int w = _edge.SecondNode.Mark;
MAP.Adjacency[v].Add(MAP.Nodes_Array[w]);
MAP.Adjacency[w].Add(MAP.Nodes_Array[v]);
}
Random rand = new Random();
bool Fitness_Func_Result = false;
Population_Fitness_Value = new int[Population_Size];
MAP_COLOR_ARRAY[] Initial_Population = new MAP_COLOR_ARRAY[Population_Size];
for (int i = 0; i < Population_Size; ++i)
{
Initial_Population[i] = new MAP_COLOR_ARRAY(Node_Quantity);
}
//Initial Random Population Making Process
for (int i = 0; i < Population_Size; i++)
{
for (int j = 0; j < Node_Quantity; j++)
{
int temp = RandomNumber(0, Color_Num - 1); //rand() % Color_Num;
Initial_Population[i].Nodes_Colors[j] = temp;
}
}
do
{
MAP_COLOR_ARRAY[] New_Population = new MAP_COLOR_ARRAY[Population_Size];
for (int i = 0; i < Population_Size; ++i)
{
New_Population[i] = new MAP_COLOR_ARRAY(Node_Quantity);
}
repeat++;
for (int i = 0; i < Population_Size; i++)
{
int x = Random_Selection(); //Using global variable Population_Fitness_Value
int y = Random_Selection();
MAP_COLOR_ARRAY Child = Reproduce(Initial_Population, x, y);
Child = Mutation(Child);
//Copy Array
for (int j = 0; j < Node_Quantity; j++)
{
New_Population[i].Nodes_Colors[j] = Child.Nodes_Colors[j];
}
}
//Copy Arrays
for (int i = 0; i < Population_Size; i++)
{
for (int j = 0; j < Node_Quantity; j++)
{
Initial_Population[i].Nodes_Colors[j] = New_Population[i].Nodes_Colors[j];
}
}
Pop_Fitness_Evaluator(Initial_Population); //Fitness & Goodness evaluator
Fitness_Func_Result = Fit_Enough();
} while (repeat < 10000 && !Fitness_Func_Result);
int Best_Individual = -1;
for (int i = 0; i < Population_Size; i++)
{
if (Population_Fitness_Value[i] == 0)
{
Best_Individual = i;
}
}
if (Best_Individual == -1)
{
//Answer Not found :C
if (!Fitness_Func_Result)
{
//если ответ не найден, наверное стоит еще раз запустить процедуру
}
}
else
{
//Print Best Population-Test
for (int i = 0; i < Node_Quantity; i++)
{
_result[i] = Initial_Population[Best_Individual].Nodes_Colors[i];
_iteration = repeat;
}
}
}
