public Solution_TSP Clone()
{
Solution_TSP clone = (Solution_TSP)this.MemberwiseClone();
clone.solution = (int[])this.solution.Clone();
return(clone);
}
public void Cross(Solution_TSP r1, double crossProb)
{
if (rand.NextDouble() < crossProb)
{
Solution_TSP c1 = new Solution_TSP(rand);
c1.solution = Enumerable.Repeat(-1, Problem_TSP.n).ToArray();
Solution_TSP c2 = new Solution_TSP(rand);
c2.solution = Enumerable.Repeat(-1, Problem_TSP.n).ToArray();
int np = rand.Next((int)Problem_TSP.n);
for (int i = 0; i < np; i++)
{
int p = rand.Next((int)Problem_TSP.n);
c1.solution[p] = this.solution[p];
c2.solution[p] = r1.solution[p];
}
int p1 = 0, p2 = 0;
for (int i = 0; i < Problem_TSP.n; i++)
{
if (c1.solution[i] == -1)
{
while (c1.solution.Contains(this.solution[p1]))
{
p1++;
}
c1.solution[i] = this.solution[p1];
}
if (c2.solution[i] == -1)
{
while (c2.solution.Contains(r1.solution[p2]))
{
p2++;
}
c2.solution[i] = r1.solution[p2];
}
}
this.solution = c1.solution;
r1.solution = c2.solution;
}
}
public GA_TSP(int popSize, double mutProb, double crossProb, Random rand)
{
pop = new Solution_TSP[popSize];
npop = new Solution_TSP[popSize];
this.mutProb = mutProb;
this.crossProb = crossProb;
this.popSize = popSize;
this.rand = rand;
for (int i = 0; i < popSize; i++)
{
pop[i] = new Solution_TSP(rand);
//Initialization
pop[i].initialize();
//Mutation
pop[i].Mutate(mutProb);
//Evaluation
pop[i].Evaluate();
cumFitness += pop[i].fitness;
}
}
