Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: Informatica
Programul de studii: Sisteme Inteligente - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MII1011	Genetic Programming and Applications	

Semester	Hours: C+S+L	Category	Status	
3	2+2+0	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
OLTEAN Mihai	Ph.D.	Lect.	Programming  Languages and Methods	*			

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
The course deals with the following notions and ideas: 

Genetic Programming, Evolutionary Computation, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Stack-based GP, Traceless Genetic Programming, NP-Complete problems, regression, classification.

-	Each student has to prove that (s)he acquired an acceptable level of knowledge and understanding of the subject, that (s)he is capable of stating these knowledge in a coherent form, that (s)he has the ability to establish certain connections and to use the knowledge in solving different problems.

IV. Course contents
1.         Introduction; Resources
2.	Basic Genetic Programming Ideas
3.	Current achievements of GP
4.	Linear Genetic Programming
5.	Gene Expression Programming
6.	Multi Expression Programming
7.	Grammatical Evolution
8.	Traceless Genetic Programming
9.	GP techniques for data analysis
10.	GP techniques for design problems
11.	GP techniques for evolving complex computer programs
12.	Open problems for GP

V. Bibliography
All about AI on the net, www.aboutai.net
[2].	GOLDBERG, D. E., Genetic Algorithm. Addison-Wesley, Reading, 1989.
[3].	DUMITRESCU, D., Algoritmi genetici si strategii evolutive. Aplicatii in Inteligenta Artificiala, Editura Albastra, Cluj, 2000.
[4].	www.genetic-programming.org
[5].	The Genetic Programming Bibliography, www.cs.bham.ac.uk/~wbl/biblio/
[6].	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. 1990
[7].	Koza, J.R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992
[8].	Koza, J.R., Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, 1994
[9].	Ferreira, C., Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. 2nd Edition, Springer-Verlag, Germany, 2006
[10].	Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D., Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann, 1998

VI. Thematic of didactic activities per weeks
1.	Administrivia; Introduction; Resources

2.	Basic Genetic Programming Ideas
-References: 1, 2, 3, 4, 5, 6

3.	Current achievements of GP
-References: 4

4.	Linear Genetic Programming
-References: 10

5.	Gene Expression Programming
-References: 9

6.	Multi Expression Programming
-References: 5

7.	Grammatical Evolution
-References: 5

8.	Traceless Genetic Programming
-References: 5

9.	First written examination

10.	GP techniques for data analysis
-References: 6, 7, 8

11.	GP techniques for design problems
-References: 6, 7, 8

12.	GP techniques for evolving complex computer programs
-References: 5

13.	Open problems for GP
-References: 5

14. 	Second written examination

Seminars

1.	Why we need GP?
- References: 1, 2, 3, 4, 5

2.	Human competitive results for GP
- References: 5

3.	Implementation for standard Genetic Programming
-References: 5

4.	Implementation for Linear GP
-References: 10

5.	Implementation for Multi Expression Programming
-References: 5

6.	Implementation for Gene Expression Programming
-References: 9

7.	Implementation for Traceless Genetic Programming
- References: 5

8.	Implementation for Grammatical Evolution
- References: 5

9.	Implementation for Stack-based GP
- References: 5

10.	Low level implementation of Genetic Programming
- References: 5

11-12.	Meta Genetic Programming
- References: 5

13-14.	Various implementations
- References: 4, 5, 6	

VII. Didactic methods used
Expunerea, conversatia, demonstratia didactica, algoritmizarea, problematizarea.	

VIII. Assessment
Two written examinations during the semester. Each of them will count as 50% from the final mark. During the examination session the student has the possibility to increase its mark.	

IX. Additional references
Other GP resources freely available on the internet.	

	Date,							Course responsible,
								Lect. OLTEAN Mihai, Ph.D.