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	
MI289	Advanced Methods in Evolutionary Computing	

Semester	Hours: C+S+L	Category	Status	
2	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
•	Describing of advanced models and techniques in evolutionary computing and their relevance for addressing complex problems;
•	Developing skills for addressing complex real-world problems;
•	Supply the background for advanced studies and research in the field;
•	Practical application of EA optimization models/techniques.	

IV. Course contents
1.	Numerical optimization
2.	Evolutionary numerical optimization
3.	Evolutionary dynamical optimization
4.	Multicriteria optimization
5.	Combinatorial optimization
6.	NP-complete problems
7.	Network optimization	

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

VI. Thematic of didactic activities per weeks
Course work 14 weeks.
Week 1.	Introduction to Numerical optimization.
Week 2.	Evolutionary numerical optimization. Problem representation. Fitness assignment. 
Week 3.	Evolutionary dynamical optimization. Noisy fitness function. Dynamic environments. Dynamic fitness function (moving peaks benchmark). 
Week 4.	Evolutionary dynamical optimization. Generating diversity. Memory based approaches. Multipopulation approaches. Hybrid approaches.
Week 5.	Evolutionary multicriteria optimization (EMOO). Basic concepts. 
Week 6.	EMOO – Standard techniques (VEGA, MOGA, NPGA, PESA)
Week 7.	EMOO – SPEA, SPEA2 algorithms
Week 8.	EMOO – NSGA, NSGA-II techniques
Week 9.	EMOO – GHEA, Roaming, PSO and DE approaches
Week 10.	EMOO – performance measures
Week 11.	Combinatorial optimization – problem statement and basic approaches
Week 12.	NP-complete problems and metaheuristics
Week 13.	Evolutionary approaches for addressing NP-hard problems.
Week 14.	Evolutionary network optimization – Network models. Dynamical network optimization/search.
Seminar
The seminar 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
1.	BACK, T.,Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996.
2.	DUMITRESCU,D.,B Lazzerini,Fuzzy Sets and treir Application in Training and Clustering , CRC Press, New York, Boca Raton, 2000 
3.	DUMITRESCU, D.,Principiile teoriei clasificarii, Editura Academiei, Bucuresti,2000.
4.	DUMITRESCU, D., Inteligenta artificiala, Univ. "Babes-Bolyai", 1995. 
5.	Miller, J. F. and Thomson, P. 2000. Cartesian Genetic Programming. In Proceedings of the European Conference on Genetic Programming (April 15 - 16, 2000). R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, Eds. Lecture Notes In Computer Science, vol. 1802. Springer-Verlag, London, 121-132.
6.	Miller, J. F., Kalganova, T., Job, D., and Lipnitskaya, N. 2002. The genetic algorithm as a discovery engine: strange circuits and new principles. In Creative Evolutionary Systems, P. J. Bentley and D. W. Corne, Eds. Morgan Kaufmann Publishers, San Francisco, CA, 443-466.
7.	Miller, J. F., Job, D., and Vassilev, V. K. 2000. Principles in the Evolutionary Design of Digital Circuits—Part I. Genetic Programming and Evolvable Machines 1, 1-2 (Apr. 2000), 7-35.
8.	Ferreira, C., 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2: 87-129
9.	Ferreira, C., 2001. Gene Expression Programming in Problem Solving, invited tutorial of the 6th Online World Conference on Soft Computing in Industrial Applications, September 10-24, 2001.
10.	Oltean, M. (et al.), Evolving Digital Circuits for the Knapsack Problem, International Conference on Computational Sciences, E-HARD Workshop, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol. III, pp. 1257-1264, 6-9 June, Krakow, Poland, Springer-Verlag, Berlin, 2004
11.	Oltean, M. (et al.), Encoding Multiple Solutions in a Linear GP Chromosome, International Conference on Computational Sciences, E-HARD Workshop, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol III, pp. 1281-1288, 6-9 June, Krakow, Poland, Springer-Verlag, Berlin, 2004
12.	Oltean M., Improving Multi Expression Programming: an Ascending Trail from Sea-level Even-3-parity Problem to Alpine Even-18-Parity Problem, chapter 10, Evolvable Machines: Theory and Applications, Springer-Verlag, edited by Nadia Nedjah (et al.), pp. 229-255, 2004
13.	Oltean M., Grosan C., Evolving Digital Circuits using Multi Expression Programming, NASA/DoD Conference on Evolvable Hardware, 24-26 June, Seattle, Edited by R. Zebulum et. al), pages 87-90, IEEE Press, NJ, 2004
14.	Markus Brameier. On linear genetic programming. Dr. rer. nat.-Dissertation, University of Dortmund, Department of Computer Science, Chair of Systems Analysis, February 2004. (Banzhaf).
15.	Zitzler, E., Deb, K., and Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation Journal, 8(2), 173-195.
16.	Corne, D. W., J. D. Knowles, and M. J. Oates, The Pareto Envelope-based Selection Algorithm for Multiobjective Optimisation In M. S. et al. (Ed.), Parallel Problem Solving from Nature - PPSN VI, Berlin, 839-848, Springer, 2000.
17.	K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan, A Fast Elitist Non- Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGAII, In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, editors, Proceedings of the Parallel Problem Solving from Nature VI Conference, 849-858, Paris, France, 2000.
18.	J. Knowles and D. Corne, The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation, In P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress on Evolutionary Computation, Mayower Hotel, Washington D.C., USA, 1:98-105, IEEE Press, 1999.
19.	E. Zitzler, M. Laumanns, and L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report 103, Gloriastrasse 35, CH-8092 Zurich, Switzerland, 2001.	
	Dean,							Chair head,

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