Overview
Lecturer
Corina Rotar
Seminar tutor
Corina Rotar
Type of course
Elective (1 of 2)
Language of instruction
English
Erasmus language
English
Domain
Computer Science
Field of study
Computer Science (in English)
Form of education
Full-time
Form of instruction
Class
Credit awarded by
Grade
Teaching methods
• Lecture, Cooperative learning, Discussion and survey, Team-based learning.
Entry requirements
• Imperative and Procedural Programming • Artificial Intelligence
Aims
• Develop the students' ability to design software that is dedicated for solving the difficult problems by exploiting evolutionary algorithms.
• Study of the algorithms that is based on natural paradigms.
• Skills for approaching the complex problems in terms of evolutionary algorithms.
• Analytical study of the advantages and disadvantages of traditional algorithms versus stochastic algorithms for optimization problems.
Course contents
1. Fundamentals of Intelligence Computation
2. Paradigm of Genetic Algorithms
3. Paradigm of Evolutionary Strategies
4. Genetic Programming. Evolutionary programming
5. Artificial Immune Systems
6. Particle Swarm Optimization Technique
7. Ants Colonies. Other natural paradigm
8. Application of evolutionary algorithms in optimization
9. Introduction to fuzzy logic. Fuzzy systems.
10. Introduction in Neural networks
11. Bio-inspired Computing and applications I
12. Bio-inspired Computing and applications II
Learning outcomes
• Implementation of an evolutionary algorithm to solve either an optimization or an NP-hard problem.
Assessment
• Final project (oral presentation) 100%
Recommended reading
• Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Inc., 1989.
• Bäck T., Evolutionary Algorithms in Theory and Practice, Oxford University Press, 1996
• Dumitrescu D., Lazzerini B., Jain L.C., Dumitrescu A., Evolutionary Computation, CRC Press, Boca Raton London, New York, Washington D.C., 2000