public void createInitialPopulation()
{
while (population.Count < POPSIZE)
{
Chromosome newChromosome = new Chromosome();
/* add any fixed events first */
foreach (Gene g in Program.fixedEvents)
newChromosome.add(g.getSlot(), g.getEvent(), g.getRoom(), g.getCannotChange());
newChromosome.scheduleLunch();
/* schedule each of the events then add the chromosome to the population */
foreach (Event e in Program.events)
newChromosome.scheduleEvent(e);
population.Add(newChromosome);
}
}
private void scheduleLists(Slot s, List<Gene> p1, List<Gene> p2, Chromosome child)
{
List<Gene> combinedList = new List<Gene>();
combinedList.AddRange(p1.Intersect(p2));
foreach (Gene g in combinedList)
{
if (p1.Contains(g))
p1.Remove(g);
if (p2.Contains(g))
p2.Contains(g);
}
combinedList.AddRange(p1);
combinedList.AddRange(p2);
combinedList.RemoveAll(delegate(Gene g) { return g.getCannotChange() == true || g.getEvent().getCourse() == "Lunch"; });
combinedList.Reverse();
for (int i = combinedList.Count; i > 0; i--)
{
bool success = child.scheduleEventFromList(s, combinedList[i - 1]);
if (!success)
child.unscheduableEvents.Add(combinedList[i - 1].getEvent());
combinedList.Remove(combinedList[i - 1]);
}
child.scheduleUnscheduableEvents(s);
}
public void crossover()
{
int numberOfGenerations = 0;
setAveFitAndStdDev();
while (numberOfGenerations < NUMGENERATIONS)
{
List<Chromosome> parents = selection();
List<Chromosome> newPopulation = new List<Chromosome>();
List<Gene> parent1;
List<Gene> parent2;
Console.WriteLine("Generation " + numberOfGenerations +
" average fitness = " + averagePopulationFitness);
while (newPopulation.Count < POPSIZE)
{
parent1 = parents[random.Next(parents.Count)].getGenes();
parent2 = parents[random.Next(parents.Count)].getGenes();
Chromosome child = new Chromosome();
/* schedule any fixed events first */
List<Gene> fixedEvents = (from g in parent1
where g.getCannotChange() == true
select g).ToList();
foreach (Gene g in fixedEvents)
child.add(g.getSlot(), g.getEvent(), g.getRoom(), g.getCannotChange());
/* schedule any lunch events next */
int[] days = { 1, 2, 4, 5 };
foreach (int day in days)
{
List<Gene> lunchEvents = (from g in parent1
where g.getEvent().getCourse() == "Lunch" &&
g.getSlot().getDay() == day
select g).ToList();
lunchEvents.AddRange(from g in parent2
where g.getEvent().getCourse() == "Lunch" &&
g.getSlot().getDay() == day
select g);
foreach (string grp in Program.groups)
{
List<Gene> slotsForGroup = (from g in lunchEvents
where g.getEvent().getGroups().Contains(grp)
select g).ToList();
Gene selectedLunchSlot = slotsForGroup[0];
/* Might have to put a check in here to ensure that not all of the groups
* (or at least a majority) have lunch at the same time */
if (slotsForGroup.Count > 1)
{
// check that there aren't any clashes (this might occur for groups with external events
bool slotFound = false;
int i = 0;
while ((i < slotsForGroup.Count) && (!slotFound))
{
if (!child.doClashesExist(slotsForGroup[i].getSlot(), slotsForGroup[i].getEvent(), slotsForGroup[i].getRoom()))
{
slotFound = true;
selectedLunchSlot = slotsForGroup[i];
}
i++;
}
}
if (!child.hasLunchBeenScheduled(selectedLunchSlot.getSlot(), selectedLunchSlot.getEvent()))
{
child.add(Program.slots[Program.slots.IndexOf(selectedLunchSlot.getSlot())],
selectedLunchSlot.getEvent(),
selectedLunchSlot.getRoom(),
selectedLunchSlot.getCannotChange());
child.add(Program.slots[Program.slots.IndexOf(child.getCorrespondingSlot(selectedLunchSlot.getSlot()))],
selectedLunchSlot.getEvent(),
selectedLunchSlot.getRoom(),
selectedLunchSlot.getCannotChange());
}
}
}
/* choose a random point from which to start going through the timetable */
Slot startSlot = Program.slots[random.Next(Program.slots.Count)];
int startPoint = Program.slots.IndexOf(startSlot);
for (int i = startPoint; i < Program.slots.Count; i++)
{
Slot s = Program.slots[i];
List<Gene> p1GenesWeekly = (from g in parent1
where (g.getSlot() == s)
select g).ToList();
List<Gene> p2GenesWeekly = (from g in parent2
where (g.getSlot() == s)
select g).ToList();
scheduleLists(s, p1GenesWeekly, p2GenesWeekly, child);
}
for (int i = 0; i < startPoint; i++)
{
Slot s = Program.slots[i];
List<Gene> p1GenesWeekly = (from g in parent1
where (g.getSlot() == s)
select g).ToList();
List<Gene> p2GenesWeekly = (from g in parent2
where (g.getSlot() == s)
select g).ToList();
scheduleLists(s, p1GenesWeekly, p2GenesWeekly, child);
}
if (random.NextDouble() < MUTATION)
child.mutate();
newPopulation.Add(child);
}
population = newPopulation;
numberOfGenerations++;
}
sortPopulation();
Console.WriteLine("\n\n" + population[0]);
}
public List<Chromosome> selection()
{
List<Chromosome> parents = new List<Chromosome>();
setAveFitAndStdDev();
/* the chance the fittest chromosome of the two selected has of being
* included in the new population*/
double selectionLimit = 0.75;
while (parents.Count < POPSIZE)
{
/* choose two random chromosomes and determine which is the fittest */
Chromosome chromosome1 = population[random.Next(population.Count)];
Chromosome chromosome2 = population[random.Next(population.Count)];
Chromosome fittestChromosome = new Chromosome();
Chromosome leastFitChromosome = new Chromosome();
if (chromosome1.chromosomeFitness() < chromosome2.chromosomeFitness())
{
fittestChromosome = chromosome1;
leastFitChromosome = chromosome2;
}
else
{
fittestChromosome = chromosome2;
leastFitChromosome = chromosome1;
}
/* if a random number is less than the selectionLimit then add the fittest
* chromosome to the population. otherwise add the least fittest */
if (random.NextDouble() < selectionLimit)
parents.Add(fittestChromosome);
else
parents.Add(leastFitChromosome);
}
return parents;
}
} // selection
public void crossover()
{
int numberOfGenerations = 0;
setAveFitAndStdDev();
while (numberOfGenerations < NUMGENERATIONS)
{
List <Chromosome> parents = selection();
List <Chromosome> newPopulation = new List <Chromosome>();
List <Gene> parent1;
List <Gene> parent2;
Console.WriteLine("Generation " + numberOfGenerations +
" average fitness = " + averagePopulationFitness);
while (newPopulation.Count < POPSIZE)
{
parent1 = parents[random.Next(parents.Count)].getGenes();
parent2 = parents[random.Next(parents.Count)].getGenes();
Chromosome child = new Chromosome();
/* schedule any fixed events first */
List <Gene> fixedEvents = (from g in parent1
where g.getCannotChange() == true
select g).ToList();
foreach (Gene g in fixedEvents)
{
child.add(g.getSlot(), g.getEvent(), g.getRoom(), g.getCannotChange());
}
/* schedule any lunch events next */
int[] days = { 1, 2, 4, 5 };
foreach (int day in days)
{
List <Gene> lunchEvents = (from g in parent1
where g.getEvent().getCourse() == "Lunch" &&
g.getSlot().getDay() == day
select g).ToList();
lunchEvents.AddRange(from g in parent2
where g.getEvent().getCourse() == "Lunch" &&
g.getSlot().getDay() == day
select g);
foreach (string grp in Program.groups)
{
List <Gene> slotsForGroup = (from g in lunchEvents
where g.getEvent().getGroups().Contains(grp)
select g).ToList();
Gene selectedLunchSlot = slotsForGroup[0];
/* Might have to put a check in here to ensure that not all of the groups
* (or at least a majority) have lunch at the same time */
if (slotsForGroup.Count > 1)
{
// check that there aren't any clashes (this might occur for groups with external events
bool slotFound = false;
int i = 0;
while ((i < slotsForGroup.Count) && (!slotFound))
{
if (!child.doClashesExist(slotsForGroup[i].getSlot(), slotsForGroup[i].getEvent(), slotsForGroup[i].getRoom()))
{
slotFound = true;
selectedLunchSlot = slotsForGroup[i];
}
i++;
}
}
if (!child.hasLunchBeenScheduled(selectedLunchSlot.getSlot(), selectedLunchSlot.getEvent()))
{
child.add(Program.slots[Program.slots.IndexOf(selectedLunchSlot.getSlot())],
selectedLunchSlot.getEvent(),
selectedLunchSlot.getRoom(),
selectedLunchSlot.getCannotChange());
child.add(Program.slots[Program.slots.IndexOf(child.getCorrespondingSlot(selectedLunchSlot.getSlot()))],
selectedLunchSlot.getEvent(),
selectedLunchSlot.getRoom(),
selectedLunchSlot.getCannotChange());
}
}
}
/* choose a random point from which to start going through the timetable */
Slot startSlot = Program.slots[random.Next(Program.slots.Count)];
int startPoint = Program.slots.IndexOf(startSlot);
for (int i = startPoint; i < Program.slots.Count; i++)
{
Slot s = Program.slots[i];
List <Gene> p1GenesWeekly = (from g in parent1
where (g.getSlot() == s)
select g).ToList();
List <Gene> p2GenesWeekly = (from g in parent2
where (g.getSlot() == s)
select g).ToList();
scheduleLists(s, p1GenesWeekly, p2GenesWeekly, child);
}
for (int i = 0; i < startPoint; i++)
{
Slot s = Program.slots[i];
List <Gene> p1GenesWeekly = (from g in parent1
where (g.getSlot() == s)
select g).ToList();
List <Gene> p2GenesWeekly = (from g in parent2
where (g.getSlot() == s)
select g).ToList();
scheduleLists(s, p1GenesWeekly, p2GenesWeekly, child);
}
if (random.NextDouble() < MUTATION)
{
child.mutate();
}
newPopulation.Add(child);
}
population = newPopulation;
numberOfGenerations++;
}
// output 100th gen fitness to file
StreamWriter SW;
SW = File.AppendText("C:\\Users\\Stu\\Desktop\\Constant analysis\\Mutation " + MUTATION + ".txt");
SW.WriteLine(averagePopulationFitness);
SW.Close();
//Console.WriteLine("Text Appended Successfully");
sortPopulation();
//Console.WriteLine("\n\n" + population[0]);
} // crossover
