Gen Schedule

Timetable scheduling using genetic algorithms.

Steps

1. Fill:

   C1 = 1  # uniformity
   C2 = 1  # tightness
   C3 = 1  # suitability
   C4 = 0.1  # sleep
   C5 = 1  # day_grouping
   C6 = 1  # week_grouping
   
   CLASSES_PER_DAY = 4
   WORKING_DAYS = 5
   PLACES = CLASSES_PER_DAY * WORKING_DAYS
   
   SUBJECTS = [
       Subject(name='AG', numbers={SubjectType.LECTURE: 1, SubjectType.SEMINAR: 2}),
       Subject(name='MA', numbers={SubjectType.LECTURE: 2, SubjectType.SEMINAR: 2}),
       Subject(name='PR', numbers={SubjectType.LECTURE: 1, SubjectType.SEMINAR: 1}),
       Subject(name='EN', numbers={SubjectType.LECTURE: 0, SubjectType.SEMINAR: 3}),
       Subject(name='DM', numbers={SubjectType.LECTURE: 1, SubjectType.SEMINAR: 1}),
   ]
   
   SUITABLE_TIME = {
       WeekDay.MON: {1: ['AG'], 2: [], 3: [], 4: []},
       WeekDay.TUE: {1: ['MA'], 2: ['PR'], 3: ['PR'], 4: []},
       WeekDay.WED: {1: ['AG'], 2: ['AG'], 3: ['MA'], 4: []},
       WeekDay.THU: {1: ['DM'], 2: ['EN'], 3: ['MA', 'EN'], 4: []},
       WeekDay.FRI: {1: ['MA'], 2: ['DM'], 3: [], 4: []}
   }

2. Run

3. Receive:

   gen	nevals	avg    	std     	min   	max    
   0  	300   	2.91481	0.291041	1.7581	3.69857
   1  	189   	3.09384	0.208402	2.2381	3.69857
   2  	171   	3.20138	0.212053	2.43392	3.69857
   3  	186   	3.24289	0.225741	2.2781 	3.69857
   4  	179   	3.28457	0.232168	1.93117	3.69857
   5  	187   	3.30843	0.244352	2.46857	3.71857
   6  	179   	3.35342	0.245704	2.32952	3.80623
   ...
   499	187   	4.1657 	0.045359 	3.68714	4.17143
   500	166   	4.15778	0.0618725	3.68714	4.17143
   
   MON:
   1)
   2) MA SEMINAR
   3) MA SEMINAR
   4) DM LECTURE
   
   TUE:
   1)
   2) PR LECTURE
   3) AG SEMINAR
   4) AG SEMINAR
   
   WED:
   1)
   2) AG LECTURE
   3) MA LECTURE
   4) MA LECTURE
   
   THU:
   1)
   2) EN SEMINAR
   3) EN SEMINAR
   4) EN SEMINAR
   
   FRI:
   1)
   2) DM SEMINAR
   3) PR SEMINAR
   4)
   

EZ.