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	Bachelor	
1.6 Study programme / Qualification 	Computer Science	

2. Information regarding the discipline 
2.1 Name of the discipline	Evolutionary Computation: Algorithms and Operators	
2.2 Course coordinator 	Lecturer PhD. Crina Grosan	
2.3 Seminar coordinator	Lecturer PhD. Crina Grosan	
2.4. Year of study	3	2.5 Semester	5	2.6. Type of evaluation	E	2.7 Type of discipline	Optional	

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 lab	
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	20	
Additional documentation (in libraries, on electronic platforms, field documentation) 	14	
Preparation for seminars/labs, homework, papers, portfolios and essays	28	
Tutorship	7	
Evaluations	10	
Other activities: ..................	-	
3.7 Total individual study hours 	62	
3.8 Total hours per semester	121	
3.9 Number of ECTS credits	6	

4. Prerequisites (if necessary)
4.1. curriculum	•	Artificial Intelligence, Graph Theory, Data Structures and Algorithms	
4.2. competencies	•	Average programming skills in a high level programming language	

5. Conditions (if necessary)
5.1. for the course	•		
5.2.  for the seminar /lab activities	•	Laboratory with computers; high level programming language environment 	

6. Specific competencies acquired 
Professional competencies	•	The basic paradigms, techniques and algorithms of Evolutionary computing and other similar methods. 
•	Evolutionary models applied for solving some tipical NP-hard problems	
Transversal competencies	•	Ability to apply avolutionary computation techniques to different real life problems
•	Ability to model problems in an interdisciplinary field

7. Objectives of the discipline (outcome of the acquired competencies)
7.1 General objective of the discipline
	•	Ability to understand and use the basic evolutionary computation and soft computing algorithms and principles.
•	Ability to model real life problems and find optimal solutions to them	
7.2 Specific objective of the discipline
	•	Acquire the knowledge about the main classes of evolutionary computation algorithms, differences, advantages of one method over the other and limitations.	

8. Content
8.1 Course	Teaching methods	Remarks	
1.	Introduction; Natural evolution
	Exposure: description, explanation, examples, discussion of case studies		
2.	Evolutionary Search Techniques

	Exposure: description, explanation, examples, discussion of case studies		
3.	Genetic Algorithms, operators, selection and parameters; Theoretical foundations, convergence and design considerations 	Exposure: description, explanation, examples, debate, dialogue		
4.	Genetic programming; Evolutionary Strategies; Evolutionary Programming	Exposure: description, explanation, examples, discussion of case studies		
5.	Swarm Intelligence; Collective behavior principles
	Exposure: description, explanation, examples, proofs		
6.	 Particle Swarm Optimization; Ant Colonies Optimization	Exposure: description, explanation, examples, proofs, debate, dialogue		
7.	Estimation of distribution algorithms
Advanced estimation distribution algorithms; Variants of EDA algorithms, UMDA, BMDA and BOA.
	Exposure: description, explanation, examples, discussion of case studies		
8.	Random search heuristics
Simulated annealing; Tabu search; Scatter search; The Metropolis algorithm	Exposure: description, explanation, examples		
9.	Multi-agent systems	Exposure: description, explanation, examples, discussion of case studies		
10.	Self adaptive systems	Exposure: description, explanation, examples, debate		
11.	Cellular automata	Exposure: description, explanation, examples, discussion of case studies		
12.	Immune systems	Exposure: description, explanation, examples, discussion of case studies		
13.	Theoretical Analysis of Evolutionary Approaches 
Convergence;  Computational time complexity; No free lunch theorems 
	Exposure: description, explanation, examples, discussion of case studies		
14.	Hybrid Approaches	Exposure: description, examples, discussion of case studies, live demo		
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.
	
8.3 Laboratory	Teaching methods	Remarks	
1.	Task: Discuss the main principles of EC, classes of algorithms, problems which can be tackled, complexity and limitations
	case studies, dialogues		
2.	Task: Implement a hybrid algorithm which should be a combination of at least two of the methods exposed during the courses. Show the limitations of each of the individual methods for the selected problem and the advantage of the hybridization.
	case studies, testing, written report		
Bibliography
GROSAN, C., Abraham, A., Intelligent Systems: a modern approach, Springer Verlag GERMANY, 2011
PATRIDGE, D., Artificial Intelligence. Aplications in the future of software engineering, Ellis Harwood Series in A.I., John Wiley & Sons, New York 1986.
RICH, E. Artificial Intelligence, Mc.Graw Hill, 1989.
WINSTON, P., Inteligenta artificiala, Ed.Tehnica, 1980.
1.	GOLDBERG, D. E., Genetic Algorithm. Addison-Wesley, Reading, 1989.	

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

•	The course follows the scheme and structure used by the most important universities in USA and Europe;
•	The course exists in the studying program of all major universities in Romania and abroad;

10. Evaluation
Type of activity	10.1 Evaluation criteria	10.2 Evaluation methods	10.3 Share in the grade (%)	
10.4 Course	- know the basic principle of the EC domain;
- apply the course concepts
- problem solving	Written exam 	50%	
10.5 Seminar/lab activities	- be able to implement a hybrid algorithm which shows increased efficiency compared to the individual algorithms combined
	-Lab assignments	50%	
10.6 Minimum performance standards	
•	At least grade 5 (from a scale of 1 to 10) at both written exam and laboratory work.	

Date		Signature of course coordinator 		Signature of seminar coordinator
..................	Lecturer PhD. Crina Grosan			Lecturer PhD. Crina Grosan
Date of approval  					  Signature of the head of department	
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