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Teaching GuideTerm Faculty of Computer Science |
| Máster Universitario en Intelixencia Artificial |
 Subjects |
| Evolutionary Computation |
| Learning aims |
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| Identifying Data | 2022/23 | |||||||||||||
| Subject | Evolutionary Computation | Code | 614544015 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 2nd four-month period |
First | Optional | 3 | ||||||||||
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| Learning outcomes | Study programme competences | ||
| Know the basic concepts of evolutionary computation, classical evolutionary algorithms and bio-inspired algorithms. | AC10 AC11 AC12 AC15 |
BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
| Have the ability to design bio-inspired and complex system models of real systems. | AC10 AC11 AC12 AC15 |
BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
| Know and apply techniques based on evolutionary systems, advanced artificial neural networks and other bio-inspired models. | AC10 AC11 AC12 AC15 |
BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
| Identify the appropriate data-driven solution search techniques according to the type of problem. Understand the different possibilities of combination or hybridization between global evolutionary search methods and other local search metaheuristics. | AC10 AC11 AC12 AC15 |
BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
| Know different bio-inspired adaptive models and handle the most current tools and work environments in the field of bio-inspired algorithms. | AC10 AC11 AC12 AC15 |
BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
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