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	
MI287	Evolutionary Algorithms 	

Semester	Hours: C+S+L	Category	Status	
1	2+2+0		compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
DUMITRESCU Dan Dumitru	Ph.D.	Prof.	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
Supply an introduction to the field studied.
Give the basic notion, techniques and algorithms.
Supply the background for advanced courses.	

IV. Course contents
1. Genetic algorithms (GAs)
2. Selection
3. Search operators for GAs
4. Population models
5. Parametter setting
6. Evolution strategies
7. Evolutionary programming
8. GAs for optimization problems (real, combinatorial, multimodal, multicriteria)	

V. Bibliography
DUMITRESCU,D.,B Lazzerini,Evolutionary Computation, CRC Press, New York, Boca Raton, 2000
DUMITRESCU,D.,B Lazzerini,Fuzzy Sets and treir Application in Training and Clustering , CRC Press, New York, Boca Raton, 2000
DUMITRESCU, D.,Principiile  Inteligentei artificiale,  Editura Albastra, Cluj,2000.
DUMITRESCU, D.,Principiile  teoriei clasificarii,  Editura Academiei, Bucuresti,2000.
DUMITRESCU, D.,Algoritmi genetici si strategii evolutive. Aplicatii in Inteligenta      Artificiala,  Editura Albastra, Cluj,2000.
DUMITRESCU, D., Inteligenta artificiala,  Univ. "Babes-Bolyai", 1995.
GOLDBERG, D. E., Genetic Algorithm. Addison-Wesley, Reading, 1989.
MICHALEWICZ, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, 1992.

VI. Thematic of didactic activities per weeks
1.	Evolutionary algorithms ;
2.	Genetic algorithms;
3.	Binary representation; population models 
4.	Search operators 
5.	Selection
6.	Evolutionary search for solving AI problems 
7.	Machine learning;
8.	Decision trees; generalized decision trees 
9.	Evolving decision trees 
10.	Pattern recognition 
11.	Evolutionary Clustering;
12.	Evolutionary learning models and techniques in AI;
13.	Evolutionary neural networks;
14.	Evolutionary neural network design;

The laboratory work follows the course structure. Methods presented in the course are discussed /compared and criticized. New ideas are encouraged and shortly evaluated.	

VII. Didactic methods used
Lectures, conversations, Projects, exercises, individual study, homework assignments	

VIII. Assessment
The activity ends with a final exam and a practical project during the semester. The final grade is the arithmetical mean of the two grades mentioned above, conditioned by each grade being at least 5. 	

IX. Additional references
GOLDBERG, D. E., Genetic Algorithms in Search, Optimization , and Machine Learning, Addison , Reading, 1989.
DUMITRESCU, D., Inteligenta artificiala,  Univ. "Babes-Bolyai", 1995.	

	Dean,							Chair head,

	Date,							Course responsible,
								Prof. DUMITRESCU Dan Dumitru, Ph.D.