// Makes new chromosome with same setup but with randomly chosen code
public Schedule MakeNewFromPrototype()
{
// make new chromosome, copy chromosome setup
var newChromosome = Copy(this, true);
// place classes at random position
var c = Configuration.CourseClasses;
int nr = Configuration.NumberOfRooms;
foreach (var courseClass in c)
{
// determine random position of class
int dur = courseClass.Duration;
int day = Configuration.Rand() % Constant.DAYS_NUM;
int room = Configuration.Rand() % nr;
int time = Configuration.Rand() % (Constant.DAY_HOURS + 1 - dur);
var reservation = new Reservation(nr, day, time, room);
// fill time-space slots, for each hour of class
for (int i = dur - 1; i >= 0; i--)
{
newChromosome.Slots[reservation.GetHashCode() + i].Add(courseClass);
}
// insert in class table of chromosome
newChromosome.Classes[courseClass] = reservation;
}
newChromosome.CalculateFitness();
return(newChromosome);
}
protected T[] Replacement(T[] population)
{
// produce offspring
var offspring = new T[_replaceByGeneration];
for (int j = 0; j < _replaceByGeneration; j++)
{
var parent = Selection(population);
offspring[j] = parent[0].Crossover(parent[1], _numberOfCrossoverPoints, _crossoverProbability);
offspring[j].Mutation(_mutationSize, _mutationProbability);
// replace chromosomes of current operation with offspring
int ci;
do
{
// select chromosome for replacement randomly
ci = Configuration.Rand() % population.Length;
// protect best chromosomes from replacement
} while (IsInBest(ci));
// replace chromosomes
population[ci] = offspring[j];
// try to add new chromosomes in best chromosome group
AddToBest(ci);
}
return(offspring);
}
protected T[] Selection(T[] population)
{
// selects parent randomly
var p1 = population[Configuration.Rand() % population.Length];
var p2 = population[Configuration.Rand() % population.Length];
return(new T[] { p1, p2 });
}
// Performs mutation on chromosome
public void Mutation(int mutationSize, float mutationProbability)
{
// check probability of mutation operation
if (Configuration.Rand() % 100 > mutationProbability)
{
return;
}
// number of classes
int numberOfClasses = Classes.Count;
int nr = Configuration.NumberOfRooms;
// move selected number of classes at random position
for (int i = mutationSize; i > 0; i--)
{
// select ranom chromosome for movement
int mpos = Configuration.Rand() % numberOfClasses;
// current time-space slot used by class
var cc1 = Classes.Keys.ElementAt(mpos);
var reservation1 = Classes[cc1];
// determine position of class randomly
int dur = cc1.Duration;
int day = Configuration.Rand() % Constant.DAYS_NUM;
int room = Configuration.Rand() % nr;
int time = Configuration.Rand() % (Constant.DAY_HOURS + 1 - dur);
var reservation2 = new Reservation(nr, day, time, room);
// move all time-space slots
for (int j = dur - 1; j >= 0; j--)
{
// remove class hour from current time-space slot
var cl = Slots[reservation1.GetHashCode() + j];
cl.RemoveAll(cc => cc == cc1);
// move class hour to new time-space slot
Slots[reservation2.GetHashCode() + j].Add(cc1);
}
// change entry of class table to point to new time-space slots
Classes[cc1] = reservation2;
}
CalculateFitness();
}
private void CreateOffspringPopulation()
{
int r1, r2, r3;
for (int i = 0; i < _populationSize; ++i)
{
do
{
r1 = Configuration.Rand(_currentArchiveSize);
} while (_archivePopulation[r1].Equals(_archivePopulation[i]));
do
{
r2 = Configuration.Rand(_currentArchiveSize);
} while (_archivePopulation[r2].Equals(_archivePopulation[i]) || r2 == r1);
do
{
r3 = Configuration.Rand(_currentArchiveSize);
} while (_archivePopulation[r3].Equals(_archivePopulation[i]) || r3 == r1 || r3 == r2);
_offspringPopulation[i] = _offspringPopulation[i].Crossover(_parentPopulation[i], _archivePopulation[r1], _archivePopulation[r2], _archivePopulation[r3], _etaCross, _crossoverProbability);
_offspringPopulation[i].Rank = _parentPopulation[i].Rank; //for rank based mutation
}
}
// Performes crossover operation using to chromosomes and returns pointer to offspring
public Schedule Crossover(Schedule parent2, int numberOfCrossoverPoints, float crossoverProbability)
{
// check probability of crossover operation
if (Configuration.Rand() % 100 > crossoverProbability)
{
// no crossover, just copy first parent
return(Copy(this, false));
}
// new chromosome object, copy chromosome setup
var n = Copy(this, true);
// number of classes
int size = Classes.Count;
var cp = new bool[size];
// determine crossover point (randomly)
for (int i = numberOfCrossoverPoints; i > 0; i--)
{
for (; ;)
{
int p = Configuration.Rand() % size;
if (!cp[p])
{
cp[p] = true;
break;
}
}
}
// make new code by combining parent codes
bool first = Configuration.Rand() % 2 == 0;
for (int i = 0; i < size; ++i)
{
if (first)
{
var courseClass = Classes.Keys.ElementAt(i);
var reservation = Classes[courseClass];
// insert class from first parent into new chromosome's class table
n.Classes[courseClass] = reservation;
// all time-space slots of class are copied
for (int j = courseClass.Duration - 1; j >= 0; j--)
{
n.Slots[reservation.GetHashCode() + j].Add(courseClass);
}
}
else
{
var courseClass = parent2.Classes.Keys.ElementAt(i);
var reservation = parent2.Classes[courseClass];
// insert class from second parent into new chromosome's class table
n.Classes[courseClass] = reservation;
// all time-space slots of class are copied
for (int j = courseClass.Duration - 1; j >= 0; j--)
{
n.Slots[reservation.GetHashCode() + j].Add(courseClass);
}
}
// crossover point
if (cp[i])
{
// change source chromosome
first = !first;
}
}
n.CalculateFitness();
// return smart pointer to offspring
return(n);
}
// Performes crossover operation using to chromosomes and returns pointer to offspring
public Schedule Crossover(Schedule parent, Schedule r1, Schedule r2, Schedule r3, float etaCross, float crossoverProbability)
{
// number of classes
int size = Classes.Count;
int jrand = Configuration.Rand(size);
// new chromosome object, copy chromosome setup
var n = Copy(this, true);
int nr = Configuration.NumberOfRooms;
for (int i = 0; i < size; ++i)
{
// check probability of crossover operation
if (Configuration.Rand() % 100 > crossoverProbability || i == jrand)
{
var courseClass = Classes.Keys.ElementAt(i);
var reservation1 = r1.Classes[courseClass];
var reservation2 = r2.Classes[courseClass];
var reservation3 = r3.Classes[courseClass];
// determine random position of class
int dur = courseClass.Duration;
int day = (int)(reservation3.Day + etaCross * (reservation1.Day - reservation2.Day));
if (day < 0)
{
day = 0;
}
else if (day >= Constant.DAYS_NUM)
{
day = Constant.DAYS_NUM - 1;
}
int room = (int)(reservation3.Room + etaCross * (reservation1.Room - reservation2.Room));
if (room < 0)
{
room = 0;
}
else if (room >= nr)
{
room = nr - 1;
}
int time = (int)(reservation3.Time + etaCross * (reservation1.Time - reservation2.Time));
if (time < 0)
{
time = 0;
}
else if (time >= (Constant.DAY_HOURS + 1 - dur))
{
time = Constant.DAY_HOURS - dur;
}
var reservation = new Reservation(nr, day, time, room);
// fill time-space slots, for each hour of class
for (int j = courseClass.Duration - 1; j >= 0; --j)
{
n.Slots[reservation.GetHashCode() + j].Add(courseClass);
}
// insert in class table of chromosome
n.Classes[courseClass] = reservation;
}
else
{
var courseClass = parent.Classes.Keys.ElementAt(i);
var reservation = parent.Classes[courseClass];
// insert class from second parent into new chromosome's class table
n.Classes[courseClass] = reservation;
// all time-space slots of class are copied
for (int j = courseClass.Duration - 1; j >= 0; --j)
{
n.Slots[reservation.GetHashCode() + j].Add(courseClass);
}
}
}
n.CalculateFitness();
// return smart pointer to offspring
return(n);
}