syllabus
1. Information regarding the programme
1.1 Higher education institution 	Babeş Bolyai University	
1.2 Faculty	Faculty of Mathematics and Computer Science	
1.3 Department	Department of Computer Science	
1.4 Field of study	Computer Science	
1.5 Study cycle	Master	
1.6 Study programme / Qualification 	Intelligent Systems	

2. Information regarding the discipline 
2.1 Name of the discipline	Genetic Programming and applications	
2.2 Course coordinator 	Oltean Mihai	
2.3 Seminar coordinator	Oltean Mihai	
2.4. Year of study	4	2.5 Semester	2	2.6. Type of evaluation	O	2.7 Type of discipline	Compulsory	

3. Total estimated time (hours/semester of didactic activities) 
3.1 Hours per week 	3	Of which: 3.2 course	2	3.3 seminar/laboratory	1	
3.4 Total hours in the curriculum 	42	Of which: 3.5 course	28	3.6 seminar/laboratory	14	
Time allotment:	hours	
Learning using manual, course support, bibliography, course notes	18	
Additional documentation (in libraries, on electronic platforms, field documentation) 	8	
Preparation for seminars/labs, homework, papers, portfolios and essays	14	
Tutorship	2	
Evaluations	2	
Other activities: ..................		
3.7 Total individual study hours 	44	
3.8 Total hours per semester	86	
3.9 Number of ECTS credits	6	

4. Prerequisites (if necessary)
4.1. curriculum	•		
4.2. competencies	•		

5. Conditions (if necessary)
5.1. for the course	•		
5.2.  for the seminar /lab activities	•		

6. Specific competencies acquired 
Professional competencies	Improved algorithmic capabilities.
Ability to work with approximation algorithms.	
Transversal competencies	Ability to extract computing ideas from the biology.	

7. Objectives of the discipline (outcome of the acquired competencies)
7.1 General objective of the discipline
	To make an introduction in the field of computing inspired from biology.	
7.2 Specific objective of the discipline
	•	To teach students how to work with approximation algorithms.	

8. Content
8.1 Course	Teaching methods	Remarks	
1.	Administria	Exposure: description, explanation, examples, discussion of case studies		
2.	Evolutionary computation	Exposure: description, explanation, examples, discussion of case studies		
3.	Evolutionary computation: representation, operators, algorithms	Exposure: description, explanation, examples, discussion of case studies		
4.	Standard Genetic Programming	Exposure: description, explanation, examples, discussion of case studies		
5.	Linear Genetic Programming	Exposure: description, explanation, examples, discussion of case studies		
6.	Multi Expression Programming	Exposure: description, explanation, examples, discussion of case studies		
7.	Gene Expression Programming	Exposure: description, explanation, examples, discussion of case studies		
8.	Grammatical Evolution	Exposure: description, explanation, examples, discussion of case studies		
9.	Cartesian GP	Exposure: description, explanation, examples, discussion of case studies		
10.	Sub machine code GP	Exposure: description, explanation, examples, discussion of case studies		
11.	Traceless GP	Exposure: description, explanation, examples, discussion of case studies		
12.	Meta GP	Exposure: description, explanation, examples, discussion of case studies		
13.	Humies	Exposure: description, explanation, examples, discussion of case studies		
14.	Presentation of projects	Discussions		
Bibliography
•	Koza, J.R., Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems, Stanford University Computer Science Department technical report STAN-CS-90-1314. A thorough report, possibly used as a draft to his 1992 book.
•	Koza, J.R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press
•	Nordin, J.P., Evolutionary Program Induction of Binary Machine Code and its Application. Krehl Verlag, Muenster, Germany.

8.2 Seminar / laboratory	Teaching methods	Remarks	
1.	Evolutionary algorithms implementation	discussion		
2.	Evolutionary algorithms implementation	Discussion		
3.	GP implementation	Discussion		
4.	LGP implementation	Discussion		
5.	MEP implementation	Discussion		
6.	GE implementation	Discussion		
7.	SMCGP implementation	Discussion		
Bibliography

	

9. Corroborating the content of the discipline with the expectations of the epistemic community, professional associations and representative employers within the field of the program

•	 

	

10. Evaluation
Type of activity	10.1 Evaluation criteria	10.2 Evaluation methods	10.3 Share in the grade (%)	
10.4 Course	Based on a project implemented during semester	oral	100%	
				
10.5 Seminar/lab activities				
				
10.6 Minimum performance standards	
•		

Date				Signature of course coordinator 	Signature of seminar coordinator
..........................		........Oltean Mihai....		...........Oltean Mihai...............

Date of approval  					  Signature of the head of department	
...........................................					...........…............................