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

Code	Subject	
MII0019	Evolutionary Computing: Algoritms and Operators	

Section	Sem.	Hours: C+S+L	Category	Type	
Computer Science - in English	5	2+0+1	speciality	optional	
Information engineering - in English	5	2+0+2	speciality	optional	

Teaching Staff in Charge	Lect. GROSAN Crina Daniela, Ph.D., cgrosan@cs.ubbcluj.ro	
Aims	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	
Content	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)

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.

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.