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
- Fundamentals of Intelligence Computation
- Paradigm of Genetic Algorithms
- Paradigm of Evolutionary Strategies
- Genetic Programming. Evolutionary programming
- Artificial Immune Systems
- Particle Swarm Optimization Technique
- Ants Colonies. Other natural paradigm
- Application of evolutionary algorithms in optimization
- Introduction to fuzzy logic. Fuzzy systems.
- Introduction in Neural networks
- Bio-inspired Computing and applications I
- 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
Genetic Algorithms in Search, Optimization, and Machine Learning
Goldberg D.E.
Addison-Wesley Publishing Company, Inc., -, 1989 · -
Evolutionary Computation
Dumitrescu D., Lazzerini B., Jain L.C., Dumitrescu A.
CRC Press, Boca Raton London, New York, Washington D.C., -, 2000 · -
Evolutionary Algorithms in Theory and Practice
Bäck T.
Oxford University Press, -, 1996 · -
Modele naturale şi algoritmi evolutivi.
Rotar C.
Accent, -, 2008 · -