// Copy constructor
public Schedule(Schedule c, bool setupOnly)
{
if (setupOnly)
{
// reserve space for time-space slots in chromosomes code
_slots = new List<CourseClass>[(DAYS_NUM * DAY_HOURS * Configuration.GetInstance.GetNumberOfRooms())];
for (int ptr = 0; ptr < (DAYS_NUM * DAY_HOURS * Configuration.GetInstance.GetNumberOfRooms()); ptr++)
_slots[ptr] = new List<CourseClass>();
// reserve space for flags of class requirements
_criteria = new bool[(Configuration.GetInstance.GetNumberOfCourseClasses() * numberOfScores)];
}
else
{
// copy code
_slots = c._slots;
_classes = c._classes;
// copy flags of class requirements
_criteria = c._criteria;
// copy fitness
_fitness = c._fitness;
}
// copy parameters
_numberOfCrossoverPoints = c._numberOfCrossoverPoints;
_mutationSize = c._mutationSize;
_crossoverProbability = c._crossoverProbability;
_mutationProbability = c._mutationProbability;
}
public CreateDataGridViews(Dictionary<int, Room> rooms, Form rFrm)
{
_schedule = null;
_running = false;
_resultWindow = rFrm;
roomList = rooms;
LastDGV_Location = new Point(12, 25);
LastLabel_Location = new Point(12, 7);
Create_FristTime();
//
// add DataGridView
//
foreach (KeyValuePair<int, DataGridView> kvp in DgvList)
{
_resultWindow.Controls["panelRoomDGV"].Controls.Add(kvp.Value);
}
//
// add Label
//
foreach (KeyValuePair<int, Label> kvp in LblRoom)
{
_resultWindow.Controls["panelRoomDGV"].Controls.Add(kvp.Value);
}
}
// Initializes genetic algorithm
public Algorithm(int numberOfChromosomes, int replaceByGeneration, int trackBest,
Schedule prototype, Schedule.ScheduleObserver observer)
{
NumberOfChromosomes = numberOfChromosomes;
TrackBest = trackBest;
ReplaceByGeneration = replaceByGeneration;
_currentBestSize = 0;
_prototype = prototype;
_observer = observer;
_currentGeneration = 0;
_state = AlgorithmState.AS_USER_STOPPED;
// reserve space for population
_chromosomes = new Schedule[NumberOfChromosomes];
_bestFlags = new bool[NumberOfChromosomes];
// reserve space for best chromosome group
_bestChromosomes = new int[TrackBest];
// clear population
for (int i = _chromosomes.Length - 1; i >= 0; --i)
{
_chromosomes[i] = null;
_bestFlags[i] = false;
}
_instance = this;
#region Find number of Active CPU or CPU core's for this Programs
long Affinity_Dec = System.Diagnostics.Process.GetCurrentProcess().ProcessorAffinity.ToInt64();
string Affinity_Bin = Convert.ToString(Affinity_Dec, 2); // toBase 2
foreach (char anyOne in Affinity_Bin.ToCharArray())
if (anyOne == '1') numCore++;
#endregion
}
// new Thread for Start GA
private void GA_Start(object Parallel_Mutex_On)
{
if ((Boolean)Parallel_Mutex_On) // Parallel Process Requirement's
{
#region GA for Mutex On
while (true) //------------------------------------------------------------------------
{
// user has stopped execution?
if (_state == AlgorithmState.AS_CRITERIA_STOPPED || _state == AlgorithmState.AS_USER_STOPPED)
{
break;
}
else if (_state == AlgorithmState.AS_SUSPENDED)
{
if (Thread.CurrentThread.IsAlive)
Thread.CurrentThread.Suspend();
}
// Save a Elite Chromosome for protection in Mutation and etc.
Schedule best = GetBestChromosome();
// algorithm has reached criteria?
if (best.GetFitness() >= 1)
{
_state = AlgorithmState.AS_CRITERIA_STOPPED;
break;
}
// produce offspring
Schedule[] offspring;
offspring = new Schedule[_replaceByGeneration];
Random rand = new Random();
for (int j = 0; j < _replaceByGeneration; j++)
{
Schedule p1;
Schedule p2;
// selects parent randomly
lock (Locker1)
{
p1 = _chromosomes[(rand.Next() % _chromosomes.Length)].MakeCopy(false);
}
lock (Locker1)
{
p2 = _chromosomes[(rand.Next() % _chromosomes.Length)].MakeCopy(false);
}
offspring[j] = p1.Crossover(p2);
lock (Locker1)
{
offspring[j].Mutation();
offspring[j].CalculateFitness();
}
}
// replace chromosomes of current operation with offspring
for (int j = 0; j < _replaceByGeneration; j++)
{
int ci;
do
{
// select chromosome for replacement randomly
ci = rand.Next() % _chromosomes.Length;
// protect best chromosomes from replacement
} while (IsInBest(ci));
lock (Locker1)
{
// replace chromosomes
_chromosomes[ci] = null;
_chromosomes[ci] = offspring[j];
}
// try to add new chromosomes in best chromosome group
AddToBest_Parallel(ci);
}
// algorithm has found new best chromosome
if (best != GetBestChromosome())
// notify observer
{
lock (Locker1)
{
_observer.NewBestChromosome(GetBestChromosome(), ResultControls.ResultForm._setting.Display_RealTime);
}
}
_currentGeneration++;
}
// The GA job's is Complete!
if (_observer != null)
{
lock (Locker0)
{
// notify observer that execution of algorithm has changed it state
_observer.EvolutionStateChanged(_state);
}
}
Thread.CurrentThread.Abort();
#endregion
}
else
{
#region GA for Mutex Off
while (true) //------------------------------------------------------------------------
{
// user has stopped execution?
if (_state == AlgorithmState.AS_CRITERIA_STOPPED || _state == AlgorithmState.AS_USER_STOPPED)
{
break;
}
else if (_state == AlgorithmState.AS_SUSPENDED)
{
if (Thread.CurrentThread.IsAlive)
Thread.CurrentThread.Suspend();
}
// Save a Elite Chromosome for protection in Mutation and etc.
Schedule best = GetBestChromosome();
// algorithm has reached criteria?
if (best.GetFitness() >= 1)
{
_state = AlgorithmState.AS_CRITERIA_STOPPED;
break;
}
// produce offspring
Schedule[] offspring;
offspring = new Schedule[_replaceByGeneration];
Random rand = new Random();
for (int j = 0; j < _replaceByGeneration; j++)
{
// selects parent randomly
Schedule p1 = _chromosomes[(rand.Next() % _chromosomes.Length)];
Schedule p2 = _chromosomes[(rand.Next() % _chromosomes.Length)];
offspring[j] = p1.Crossover(p2);
offspring[j].Mutation();
offspring[j].CalculateFitness();
}
// replace chromosomes of current operation with offspring
for (int j = 0; j < _replaceByGeneration; j++)
{
int ci;
do
{
// select chromosome for replacement randomly
ci = rand.Next() % _chromosomes.Length;
// protect best chromosomes from replacement
} while (IsInBest(ci));
// replace chromosomes
_chromosomes[ci] = null;
_chromosomes[ci] = offspring[j];
// try to add new chromosomes in best chromosome group
AddToBest_Sequence(ci);
}
// algorithm has found new best chromosome
if (best != GetBestChromosome())
// notify observer
{
_observer.NewBestChromosome(GetBestChromosome(), ResultControls.ResultForm._setting.Display_RealTime);
}
_currentGeneration++;
}
// The GA job's is Complete!
if (_observer != null)
{
// notify observer that execution of algorithm has changed it state
_observer.EvolutionStateChanged(_state);
}
Thread.CurrentThread.Abort();
#endregion
}
}
// Returns reference to global instance of algorithm
public static Algorithm GetInstance()
{
// global instance doesn't exist?
if (_instance == null)
{
// make prototype of chromosomes
Schedule prototype = new Schedule(5, 5, 90, 10);
// make new global instance of algorithm using chromosome prototype
_instance = new Algorithm(1000, 180, 50, prototype, new Schedule.ScheduleObserver());
}
return _instance;
}
private void Save(Schedule schedule)
{
int numberOfRooms = Configuration.GetInstance.GetNumberOfRooms();
int daySize = schedule.day_Hours * numberOfRooms;
var db = new LINQDataContext();
db.Classroom_TimeDeleteAll();
foreach (KeyValuePair<CourseClass, int> it in schedule.GetClasses().ToList())
{
// coordinate of time-space slot
int pos = it.Value; // int pos of _slot array
int day = pos / daySize;
int time = pos % daySize; // this is not time now!
int room = time / schedule.day_Hours;
time = time % schedule.day_Hours; // this is a time now!
int dur = it.Key.GetDuration;
CourseClass cc = it.Key;
Algorithm.Room r = Configuration.GetInstance.GetRoomById(room);
//
// Save Classroom_Time
//
db.Classroom_TimeSave(r.Origin_ID_inDB, cc.Class_ID, cc.GetProfessor.GetId, time, dur, day);
//
// Save New_GroupsPerClassroom
//
foreach (var gs in cc.GetGroups)
{
db.New_GroupsPerClassSave(r.Origin_ID_inDB, cc.Class_ID, time, day, gs.GetId);
}
}
}
private void ResultForm_Load(object sender, EventArgs e)
{
Configuration.GetInstance.ParseFile(new LINQDataContext());
btnPause.Enabled = false;
btnStop.Enabled = false;
if (Configuration.GetInstance.GetNumberOfRooms() > 0)
{
create_GridView = new CreateDataGridViews(Configuration.GetInstance.Rooms, this);
Schedule prototype = new Schedule(5, 5, 90, 10);
Schedule.ScheduleObserver sso = new Schedule.ScheduleObserver();
sso.SetWindow(create_GridView);
AA = new MakeClassSchedule.Algorithm.Algorithm(1000, 180, 50, prototype, sso);
state = ThreadState.Unstarted;
timerWorkingSet.Start();
}
else
{
MessageBox.Show("Number of rooms is less than the limit!", "Number of Rooms Error",
MessageBoxButtons.OK, MessageBoxIcon.Error);
timerWorkingSet.Stop();
AA = null;
Dispose();
return;
}
if (Configuration.GetInstance.GetNumberOfCourseClasses() <= 0)
{
btnStart.Enabled = false;
}
}
public void SetSchedule(Schedule schedule, bool showGraphical)
{
_schedule = schedule.MakeCopy(false);
if (Monitor.TryEnter(Locker, 500))
{
//_resultWindow.Controls["lblFitness"].Text = schedule.GetFitness().ToString();
SetText("Fitness: " + schedule.GetFitness().ToString());
Monitor.Exit(Locker);
}
else return;
if (showGraphical)
{
//
// ReSet to New DataGridView
//
foreach (KeyValuePair<int, DataGridView> it in dgvList)
{
ClearDataGridView(it.Value);
}
//
int numberOfRooms = Configuration.GetInstance.GetNumberOfRooms();
int daySize = schedule.day_Hours * numberOfRooms;
Random rand = new Random();
foreach (KeyValuePair<CourseClass, int> it in schedule.GetClasses().ToList())
{
// coordinate of time-space slot
int pos = it.Value; // int pos of _slot array
int day = pos / daySize;
int time = pos % daySize; // this is not time now!
int room = time / schedule.day_Hours;
time = time % schedule.day_Hours; // this is a time now!
int dur = it.Key.GetDuration;
CourseClass cc = it.Key;
Room r = Configuration.GetInstance.GetRoomById(room);
string groups_Name = "";
foreach (var gs in cc.GetGroups)
{
groups_Name += gs.GetName + "\r\n";
}
groups_Name = groups_Name.Trim();
((DataGridViewTextBoxCellEx)dgvList[r.GetId][day + 1, time]).RowSpan = cc.GetDuration;
dgvList[r.GetId][day + 1, time].Style.BackColor =
Color.FromArgb(rand.Next(70, 250), rand.Next(70, 250), rand.Next(70, 250));
dgvList[r.GetId][day + 1, time].Value = string.Format(CultureInfo.CurrentCulture,
"{0}\r\n{1}\r\n{2}\r\n{3}", cc.GetCourse.GetName, cc.GetProfessor.GetName, groups_Name, cc.Lab);
//(cc.GetCourse.GetName + Environment.NewLine +
// cc.GetProfessor.GetName + Environment.NewLine +
// groups_Name + Environment.NewLine + cc.Lab);
}
}
}
// Handles event that is raised when algorithm finds new best chromosome
public void NewBestChromosome(Schedule newChromosome, bool showGraphical)
{
if (_window.DgvList.Count > 0)
_window.SetSchedule(newChromosome, showGraphical);
}
// Makes new chromosome with same setup but with randomly chosen code
public Schedule MakeNewFromPrototype()
{
// number of time-space slots
int size = _slots.Length;
// make new chromosome, copy chromosome setup
Schedule newChromosome = new Schedule(this, true);
// place classes at random position
List<CourseClass> cc = Configuration.GetInstance.GetCourseClasses();
foreach (CourseClass it in cc)
{
// determine random position of class
int num_rooms = Configuration.GetInstance.GetNumberOfRooms();
int dur = it.GetDuration;
Random rand = new Random();
int day = rand.Next() % DAYS_NUM;
int room = rand.Next() % num_rooms;
int time = rand.Next() % (DAY_HOURS + 1 - dur);
int pos = (day * num_rooms * DAY_HOURS) + (room * DAY_HOURS + time); // (Base) + (offset) time's
// fill time-space slots, for each hour of class
for (int i = dur - 1; i >= 0; i--)
newChromosome._slots[pos + i].Add(it);
// insert in class table of chromosome
newChromosome._classes.Add(it, pos);
}
newChromosome.CalculateFitness();
// return smart pointer
return newChromosome;
}
// Performed crossover operation using to chromosomes
// and returns pointer to offspring
public Schedule Crossover(Schedule parent2)
{
Random rand = new Random();
// check probability of crossover operation
if (rand.Next() % 100 > _crossoverProbability)
// no crossover, just copy first parent
return new Schedule(this, false);
// new chromosome object, copy chromosome setup
Schedule n = new Schedule(this, true);
// number of classes
int size = _classes.Count;
bool[] cp = new bool[size];
// determine crossover point (randomly)
for (int i = _numberOfCrossoverPoints; i > 0; i--)
{
while (true)
{
int p = rand.Next() % size;
if (!cp[p])
{
cp[p] = true;
break;
}
}
}
//Dictionary<CourseClass, int> it1 = _classes;
List<KeyValuePair<CourseClass, int>> it1 = _classes.ToList<KeyValuePair<CourseClass, int>>();
//Dictionary<CourseClass, int> it2 = parent2._classes;
List<KeyValuePair<CourseClass, int>> it2 = parent2._classes.ToList<KeyValuePair<CourseClass, int>>();
// make new code by combining parent codes
bool first = (rand.Next() % 2 == 0);
//
for (int i = 0; i < size; i++)
{
if (first)
{
// insert class from first parent into new chromosome's class table
n._classes.Add(it1[i].Key, it1[i].Value);
// all time-space slots of class are copied
for (int j = it1[i].Key.GetDuration - 1; j >= 0; j--)
n._slots[it1[i].Value + j].Add(it1[i].Key);
}
else
{
// insert class from second parent into new chromosome's class table
n._classes.Add(it2[i].Key, it2[i].Value);
// all time-space slots of class are copied
for (int j = it2[i].Key.GetDuration - 1; j >= 0; j--)
n._slots[it2[i].Value + j].Add(it2[i].Key);
}
// crossover point
if (cp[i])
// change source chromosome
first = !first;
}
n.CalculateFitness();
// return smart pointer to offspring
return n;
}
