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

SYLLABUS

I. General data
Code	Subject	
MII0019	Evolutionary Computing: Algoritms and Operators	

Semester	Hours: C+S+L+P	Category	Status	
5	2+0+1+0	speciality	optional	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
GROSAN Crina Daniela	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 basic paradigms, techniques and algorithms of Evolutionary computing and other similar methods are presented. 
Evolutionary models are applied for solving some tipical NP-hard problems	

IV. Course contents
1. Introduction
     1.1 Natural evolution; 1.2 Evolutionary Algorithms basics

2. Evolutionary Search Techniques
2.1 Genetic Algorithms, operators, selection and parameters; 2.2Theoretical foundations, convergence and design considerations 2.3 Genetic programming; 2.4 Evolutionary Strategies; 2.5Evolutionary Programming

3. Swarm Intelligence
3.1 Collective behavior principles; 3.2 Particle Swarm Optimization; 3.3 Ant Colonies Optimization

4. Estimation of distribution algorithms
4.1 Advanced estimation distribution algorithms; 4.2Variants of EDA algorithms, UMDA, BMDA and BOA.
5. Random search heuristics
5.1	Simulated annealing; 5.2 Tabu search; 5.3 Scatter search; 5.4 The Metropolis algorithm

6. Multi-agent systems

7. Self adaptive systems

8. Cellular automata

9. Immune systems

10. Theoretical Analysis of Evolutionary Approaches 
10.1	 Convergence; 10.2 Computational time complexity; 10.3 No free lunch theorem 

11. Hybrid Approaches
11.1 Hybridization between an evolutionary algorithm and another evolutionary
Algorithm; 11.2. Neural network assisted evolutionary algorithms; 11.3. Fuzzy logic assisted evolutionary algorithm 11.4 PSO assisted evolutionary algorithm; 11.5. ACO assisted evolutionary algorithm; 11.6. Bacterial foraging optimization assisted evolutionary algorithm; 11.7. Hybridization between evolutionary algorithm and other heuristics (such as local
search, tabu search, simulated annealing, hill climbing, dynamic programming, greedy random adaptive search procedure, etc)

V. Bibliography
1. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, Heilderberg, Germany, 2003.
2. T. Back, D.B. Fogel, and Z. Michalewicz (Eds), Evolutionary Computation: Basic Algorithms and Operators, Vol. 1 and Vol. 2, Institute of Physics Publishing, Philadelphia, PA, 2000.
3. J. R. Koza et al., Genetic Programming IV, Kluwer, Norwell, MA, 2003.
1. R. Sarker and M. Mohammadian, and X. Yao (Eds), Evolutionary Optimization, Kluwer, Norwell, MA, 2002
2. R. Riolo and B. Worzel (Eds), Genetic Programming Theory and Practice, Norwell, MA, 2003.
3. Y.C. Jin (Ed.), Knowledge Incorporation in Evolutionary Computation, Springer, New York, 2005.

Other materials will be provided during the course and/or labs such as journal and conference papers, reports, etc.

VI. Thematic of didactic activities per weeks

VII. Didactic methods used

VIII. Assessment
There will be just one lab project which is to be completed by the end of the semester. Lab project should be selected from the list provided. The students have the option to present a proposal for a project of their own but the proposal must be presented for approval and must include a detailed description of the work to be undertaken along with a statement of personal motivation for the proposed work.
There will also be a written examination from the course material.
The final mark will consist of the avarage between the lab and exam@s marks.	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Lect. GROSAN Crina Daniela, Ph.D.