public GAIndividual best_ind; // best individual of this evolution
public GAEvolve(int generations, int pop_size, int nvert, int xrate, int mrate)
{
// xrate: cross-over rate, the initial value
// mrate: mutation rate, the initial value
// xrate and mrate may be adapted over generations
// nvert: number of graph vertices
best_fitness = new float[generations];
worst_fitness = new float[generations];
avr_fitness = new float[generations];
GAPopulation gap = new GAPopulation(pop_size, nvert);
best_fitness[0] = gap.ind[gap.best_index].fitness;
worst_fitness[0] = gap.ind[gap.worst_index].fitness;
avr_fitness[0] = gap.avr_fitness;
for (int i = 1; i < generations; i++)
{
gap = GAPopulation.generate(gap, xrate, mrate);
best_fitness[i] = gap.ind[gap.best_index].fitness;
worst_fitness[i] = gap.ind[gap.worst_index].fitness;
avr_fitness[i] = gap.avr_fitness;
}
best_ind = gap.ind[gap.best_index];
}
public static void Main()
{
GAIndividual.adj_matrix = GAUtils.readMatrix(@"d:\graph.txt");
int nvert = GAIndividual.adj_matrix.Length; // number of vertices
GAPopulation gap = new GAPopulation(100, nvert);
for (int i = 0; i < 100; i++)
{
Console.WriteLine(gap.ind[gap.best_index]);
gap = GAPopulation.generate(gap, 60, 30);
}
Console.WriteLine(gap.ind[gap.best_index]);
}
public static GAPopulation generate(GAPopulation p, int xrate, int mrate)
{
// Generate a new population from p, xrate percent of the induviduals
// of new population are generated by cross-over, mrate percent of them are
// generated by mutation, and others by reproduction.
if (xrate < 0 || xrate > 100 || mrate < 0 || mrate > 100 || xrate + mrate > 100)
{
Console.Error.WriteLine("error: xrate and/or mrate are not properly set");
}
GAIndividual[] newg = new GAIndividual[p.pop_size];
int newg_index = 0;
int xn = xrate * p.pop_size / 100; // xn: number of offsprings to be produced by xover
int mn = mrate * p.pop_size / 100; // mn: number of offsprings to be produced by mutation
// xover:
for (int i = 0; i < xn; i++)
{
// select two parents for cross-over:
// we want two distinct parents (i.e. p1 != p2)
int p1, p2;
do
{
p1 = p.tr_select();
p2 = p.tr_select();
}while (p1 == p2);
newg[newg_index++] = GAIndividual.xover1p(p.ind[p1], p.ind[p2]);
}
// mutation:
for (int i = 0; i < mn; i++)
{
newg[newg_index++] = p.ind[p.tr_select()].mutate();
}
// reproduction:
for (int i = newg_index; i < p.pop_size; i++)
{
newg[i] = p.ind[p.tr_select()];
}
return(new GAPopulation(newg));
}
void Awake()
{
InfluenceMap.getInstance().center = new Vector2(center.x, center.z);
InfluenceMap.getInstance().DefaultY = center.y;
InfluenceMap.getInstance().height = height;
InfluenceMap.getInstance().width = width;
InfluenceMap.getInstance().htileCount = h_Tile;
InfluenceMap.getInstance().wtileCount = w_Tile;
InfluenceMap.getInstance().mPlane = mPlane;
InfluenceMap.getInstance().Init();
GAPopulation.GlobalInit();
GAPopulationManager.getInstance().Init();
Type type = Type.GetType(DemoWorldName);
aiWorld = (AIWorld)type.Assembly.CreateInstance(DemoWorldName);
aiWorld.Init();
}
