Course 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 Entry Requirements:

Imperative and Procedural Programming, Artificial Intelligence

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

Teaching methods:

Lecture, Cooperative learning, Discussion and survey, Team-based learning.

Learning outcomes:

Implementation of an evolutionary algorithm to solve either an optimization or an NP-hard problem.

Learning outcomes verification and assessment criteria:

Final project (oral presentation) 100%

Recommended reading:

Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Inc., -, 1989, -.
Dumitrescu D., Lazzerini B., Jain L.C., Dumitrescu A., Evolutionary Computation, CRC Press, Boca Raton London, New York, Washington D.C., -, 2000, -.
Bäck T., Evolutionary Algorithms in Theory and Practice, Oxford University Press, -, 1996, -.
Rotar C., Modele naturale şi algoritmi evolutivi., Accent, -, 2008, -.