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
Semester
Hours: C+S+L
Category
Type
Computer Science - in English
5
2+0+2
speciality
optional
Teaching Staff in Charge
Lect. GROSAN Crina Daniela, Ph.D.,  cgrosancs.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)

References
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.
Links: Syllabus for all subjects
Romanian version for this subject
Rtf format for this subject