static void DisplaySchedule(ScheduleOld s)
{
int daySize = Constants.HOURS_PER_DAY * Configuration.GetInstance().Rooms.Count;
var classes = from v in s.Classes
let day = v.Value / daySize
let time = v.Value % daySize
let room = time / Constants.HOURS_PER_DAY
let finaltime = time % Constants.HOURS_PER_DAY
select new { Day = day, Professor = v.Key.ClassProfessor, Time = finaltime, Course = v.Key.ClassCourse, Duration = v.Key.Length, Room = Configuration.GetInstance().GetRoomByID(room), Group = v.Key.StudentGroups[0] };
var sortedclasses = from c in classes
orderby c.Time ascending
orderby c.Day ascending
group c by c.Group into a
from e in a
group e by e.Day;
Console.WriteLine(" {0, -12} {1, -7} {2, -15} {3, -15} {4,-4} {5, -4}", "Time", "Group", "Course", "Professor", "Room", "Lab");
foreach (var day in sortedclasses)
{
Console.WriteLine("Day {0}:", day.Key);
foreach(var c in day)
{
string line = string.Format("{0, -12} {1, -7} {2, -15} {3, -15} {4,-4} {5,-4}", TimeSpanToString(GetTime(c.Time)) + "-" + TimeSpanToString(GetTime(c.Time+c.Duration)), c.Group.Name, c.Course.Name, c.Professor.Name, c.Room.Name, c.Room.IsLab ? "Yes":"No");
Console.WriteLine(" {0}", line);
}
}
}
public static Algorithm GetInstance()
{
if (_instance == null)
{
ScheduleOld prototype = new ScheduleOld(2, 2, 40, 2);
_instance = new Algorithm(100, 5, 5, prototype);
}
return _instance;
}
static void a_NewBestChromosome(object sender, ChromosomeEventArgs e)
{
LastBest = (sender as Algorithm).GetBestChromosome().Clone(false);
Console.WriteLine("New best chromosome found: fitness {0}", (sender as Algorithm).GetBestChromosome().Fitness);
DisplaySchedule(e.Chromosome);
if (e.Chromosome.Fitness > 100.9)
{
LastBest = e.Chromosome;
(sender as Algorithm).Stop();
}
//DisplaySchedule((sender as Algorithm).GetBestChromosome());
//Console.ReadLine();
}
public ScheduleOld(ScheduleOld c, bool shallow)
{
InitializeListCapacities();
if (!shallow)
{
for (int i = 0; i < c._timeslots.Length; i++)
{
foreach (var v in c._timeslots[i])
{
_timeslots[i].Add(v.Clone());
}
}
foreach (var k in c._classes)
{
_classes.Add(k.Key.Clone(), k.Value);
}
_criteria = c._criteria;
_fitness = c._fitness;
}
_numberOfCrossoverPoints = c._numberOfCrossoverPoints;
_mutationSize = c._mutationSize;
_crossoverProbability = c._crossoverProbability;
_mutationProbability = c._mutationProbability;
}
public void Start()
{
if (_prototype==null) return;
State = AlgorithmState.RUNNING;
ClearBest();
for (int i = 0; i < _numberOfChromosomes; i++)
{
_chromosomes.Add(_prototype.CreateNewFromPrototype());
AddToBest(i);
}
_currentGeneration = 0;
while (true)
{
if (_state != AlgorithmState.RUNNING)
{
break;
}
ScheduleOld best = GetBestChromosome();
if (best.Fitness >= 120)
{
State = AlgorithmState.CRITERIA_STOPPED;
break;
}
ScheduleOld[] offspring = new ScheduleOld[_replaceByGeneration];
for (int j = 0; j < _replaceByGeneration; j++)
{
ScheduleOld p1 = _chromosomes[_random.Next(_chromosomes.Count)];
ScheduleOld p2 = _chromosomes[_random.Next(_chromosomes.Count)];
offspring[j] = p1.CrossOver(p2);
offspring[j].Mutation();
offspring[j].Repair();
}
for (int j = 0; j < _replaceByGeneration; j++)
{
int ci=0;
do
{
ci = _random.Next(_chromosomes.Count);
}
while (IsInBest(ci));
_chromosomes[ci] = offspring[j];
AddToBest(ci);
}
var newbest = GetBestChromosome();
if (best != newbest)
{
OnNewBestChromosome(newbest);
}
_currentGeneration++;
OnEvolutionStateChanged(EventArgs.Empty);
}
}
void OnNewBestChromosome(ScheduleOld s)
{
NewBestChromosome(this, new ChromosomeEventArgs(s));
}
public ChromosomeEventArgs(ScheduleOld s)
: base()
{
_chromosome = s;
}
public Algorithm(int numberOfChromosomes, int replaceByGeneration, int trackBest, ScheduleOld prototype)
{
_replaceByGeneration = replaceByGeneration;
_prototype = prototype;
if (numberOfChromosomes < 2)
numberOfChromosomes = 2;
if (trackBest < 1)
trackBest = 1;
if (_replaceByGeneration < 1)
_replaceByGeneration = 1;
else if (_replaceByGeneration > numberOfChromosomes - trackBest)
_replaceByGeneration = numberOfChromosomes - trackBest;
_numberOfChromosomes = numberOfChromosomes;
_chromosomes = new List<ScheduleOld>(numberOfChromosomes);
_bestFlags = new bool[numberOfChromosomes];
_bestChromosomes = new int[trackBest];
}
public ScheduleOld CrossOver(ScheduleOld parent2)
{
if (_random.Next(100) > _crossoverProbability)
return new ScheduleOld(this, false);
ScheduleOld s = new ScheduleOld(this, true);
int numberOfClasses = _classes.Count;
bool[] cp = new bool[numberOfClasses];
for (int i = _numberOfCrossoverPoints; i > 0; i--)
{
while (true)
{
int p = _random.Next(numberOfClasses);
if (!cp[p])
{
cp[p] = true;
break;
}
}
}
var classes1 = _classes.GetEnumerator();
var classes2 = parent2._classes.GetEnumerator();
bool first = _random.Next(0,2)==0;
for (int i = 0; i < numberOfClasses; i++)
{
classes1.MoveNext();
classes2.MoveNext();
if (first)
{
s._classes.Add(classes1.Current.Key, classes1.Current.Value);
for (int j = classes1.Current.Key.Length - 1; j >= 0; j--)
{
s._timeslots[classes1.Current.Value + j].Add(classes1.Current.Key);
}
}
else
{
s._classes.Add(classes2.Current.Key, classes2.Current.Value);
for (int j = classes2.Current.Key.Length - 1; j >= 0; j--)
{
s._timeslots[classes2.Current.Value + j].Add(classes2.Current.Key);
}
}
if (cp[i]) first = !first;
}
s.CalculateFitness();
return s;
}
public ScheduleOld CreateNewFromPrototype()
{
int size = _timeslots.Length;
ScheduleOld newChromosome = new ScheduleOld(this, true);
List<CourseClass> classes = Configuration.GetInstance().CourseClasses;
int roomCount = Configuration.GetInstance().Rooms.Count;
foreach (CourseClass c in classes)
{
int duration = c.Length;
int day = _random.Next(Constants.DAYS);
int room = c.RequiresLab ? Configuration.GetInstance().GetLabRoom().ID : Configuration.GetInstance().GetNonLabRoom().ID;
int time = _random.Next(Constants.HOURS_PER_DAY + 1 - duration);
int pos = day * roomCount * Constants.HOURS_PER_DAY + room * Constants.HOURS_PER_DAY + time;
for(int i = duration-1;i>=0;i--)
{
newChromosome._timeslots[pos+i].Add(c);
}
newChromosome._classes.Add(c, pos);
}
newChromosome.CalculateFitness();
return newChromosome;
}
