Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Master
SUBJECT

Code	Subject	
MMC1010	Stochastic Search Methods	

Section	Semester	Hours: C+S+L	Category	Type	
Optimization of computational models- in Hungarian	1	2+2+0		compulsory	
Optimization of computational models- in Hungarian	1	2+2+0	speciality	compulsory	

Teaching Staff in Charge	Assoc.Prof. SOOS Anna, Ph.D., asoos@math.ubbcluj.ro	
Aims	Introduction in stochastic search and optimization methods and algorithms. To use these algorithms for solving real problems.	
Content	Stochastic processes. Markov chains. Gaussian processes.
Introduction to stochastic search and optimization.
Local and global optima. Computer simulation.
Stochastic optimization algoritms. Convergence of them.
Stochastic gradient method.
Simulated annealing. Metropolis algorithm.
Genetic algorithms. Schema theorem. Convergence of this algorithm.
Evolutionary strategies. Evolutionary programming.
Tabu  search.  Binary search.
Aplications.
Markov chains and Monte Carlo methods.	
Bibliography	1.	A. Almos, S. Gyori, G. Horvath, A. Koczy: Genetikus algoritmusok, Typotex, 2002
2.	Thomas Bäck. Evolutionary algorithms in theory and practice. OxfordUniversity Press, New York, 1996.
3.	M. Mitzenmacher, E. Upfal: Probability and Computing, Cambridge University Press, 2005  
4.	A. Noga: The probabilistic method, Wiley,  2001
5.	H.P. Schwefel. Evolution and Optimum Seeking. Wiley, 1995.
6.	 A. Soós:  A valószínűségszámítás elemei, Egyetemi Kiadó, Kolozsvár, 2001  
7.	J.C. Spall: Introduction to Stochastic Search and Optimization, Amazon, 2002

Assessment	3 problems solved by computer, 30%.
Solving a real problem by stochastic algorithm 70%.