/// <summary>
/// Výběrová funkce => Turnaj mezi 2 jedinci
/// Náhodně vyberu 2 jedince z celé populace.
/// Podle getDistance funkce vyberu lepšího jedince. (Kratší vzdálenost = lepší)
/// </summary>
/// <returns>Invidual (Jedinec)</returns>
private Invidual select()
{
var rnd = new Random();
int i1 = rnd.Next(0, Algorithm.POPULATION_SIZE);
int i2 = rnd.Next(0, Algorithm.POPULATION_SIZE);
while (i1 == i2)
{
i2 = rnd.Next(0, Algorithm.POPULATION_SIZE);
}
Invidual a = this.pCurrent[i1];
Invidual b = this.pCurrent[i2];
if (a.getDistance() < b.getDistance())
{
return(a);
}
else
{
return(b);
}
}
/// <summary>
/// Konstruktor s argumentem -> Tvorba jedince ze 2 rodičů
/// </summary>
/// <param name="p1">Rodič 1</param>
/// <param name="p2">Rodič 2</param>
public Invidual(Invidual p1, Invidual p2)
{
var rnd = new Random();
int a = rnd.Next(0, 2);
if (a == 0)
{
for (int i = 0; i < Algorithm.CROSSIN_POINT; i++)
{
this.sequence[i] = p1.sequence[i];
}
for (int i = Algorithm.CROSSIN_POINT; i < Algorithm.LENGTH; i++)
{
this.sequence[i] = p2.sequence[i];
}
}
else
{
for (int i = 0; i < Algorithm.CROSSIN_POINT; i++)
{
this.sequence[i] = p2.sequence[i];
}
for (int i = Algorithm.CROSSIN_POINT; i < Algorithm.LENGTH; i++)
{
this.sequence[i] = p1.sequence[i];
}
}
if (getDuplicity() > 0)
{
fixMe();
}
}
/// <summary>
/// Konstruktor
/// Naplním populaci jedinci
/// Na začátku jsou jedinci generováni náhodně
/// </summary>
public Population()
{
this.pCurrent = new List <Invidual>();
this.pNext = new List <Invidual>();
this.best = null;
for (int i = 0; i < Algorithm.POPULATION_SIZE; i++)
{
this.pCurrent.Add(new Invidual());
}
this.updateBest();
}
/// <summary>
/// Projdu celou populaci a aktializuji nejlepšího jedince.
/// Tohoto jedince držím v proměnné Invidual best
/// </summary>
private void updateBest()
{
this.best = null;
foreach (Invidual iTmp in this.pCurrent)
{
if (this.best == null)
{
this.best = iTmp;
}
else
{
if (iTmp.getDistance() < this.best.getDistance())
{
this.best = iTmp;
}
}
}
}
/// <summary>
/// Zlepšující funkce
/// S 80% šancí (nebo Algorithm.P_CROSSOVER) aplikuji operátor křížení
/// S 20% šancí (nebo Algorithm.P_MUTATION) apliukuji operátor mutace
/// Aktualizuji populaci po křížení a mutaci
/// </summary>
public void improve()
{
var rnd = new Random();
this.pNext.Clear();
foreach (Invidual iTmp in this.pCurrent)
{
Invidual iNew;
if (rnd.Next(1, 100) < Algorithm.P_CROSSOVER)
{
iNew = new Invidual(this.select(), this.select());
}
else
{
iNew = this.select();
}
if (rnd.Next(1, 100) < Algorithm.P_MUTATION)
{
iNew.mutate();
}
this.pNext.Add(iNew);
}
this.pCurrent.Clear();
foreach (Invidual iTmp in this.pNext)
{
this.pCurrent.Add(iTmp);
}
this.updateBest();
}
