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COMPUTER ENGINEERING (MS) (ENGLISH) PROGRAMME COURSE DESCRIPTION |
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| Name of the Course Unit | Code | Year | Semester | In-Class Hours (T+P) | Credit | ECTS Credit |
|---|---|---|---|---|---|---|
| EVOLUTIONARY OPTIMIZATION STRATEGIES | CMP511 | 1 | 2 | 3+0 | 3.0 | 8.0 |
| No |
Key Learning Outcomes of the Course Unit On successful completion of this course unit, students/learners will or will be able to: |
PROGRAMME LEARNING OUTCOMES |
|---|---|---|
| 1 | will learn the theory, history, mathematics and programming of evolutionary optimization algorithms | 1 (5), 2 (3), 3 (3), 4 (4), 5 (4), 6 (4), 7 (4), 8 (3), 9 (3), 10 (3), 11 (4), 12 (3) |
| 2 | will learn programming with genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization and convex optimization techniques | 1 (5), 2 (3), 3 (3), 4 (4), 5 (5), 6 (4), 7 (4), 8 (3), 9 (5), 10 (3), 11 (5), 12 (3), 13 (5) |
| 3 | Mathematics and science knowledge and understanding in engineering problems | 1 (5), 2 (3), 3 (3), 4 (4), 5 (4), 6 (4), 7 (4), 8 (3), 9 (3), 10 (3), 11 (4), 12 (3) |
| 4 | Ability to analyze and interpret data on complex engineering systems in a multidisciplinary context. | 1 (5), 2 (5), 3 (5), 4 (5), 5 (4), 6 (5), 7 (5), 8 (5), 9 (3), 10 (5), 11 (4), 12 (3) |
| 5 | Ability to identify, formulate and solve unfamiliar complex engineering problems, to apply systems thinking in complex problem solving, to learn independently for life, undertake further work autonomously | 1 (5), 2 (3), 3 (3), 4 (4), 5 (4), 6 (4), 7 (4), 8 (3), 9 (3), 10 (3), 11 (4), 12 (3) |