|
Teaching GuideTerm Faculty of Computer Science |
| Máster Universitario en Intelixencia Artificial |
 Subjects |
| Evolutionary Computation |
| Contents |
|
|
| Identifying Data | 2022/23 | |||||||||||||
| Subject | Evolutionary Computation | Code | 614544015 | |||||||||||
| Study programme |
|
|||||||||||||
| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 2nd four-month period |
First | Optional | 3 | ||||||||||
|
||||||||||||||
| Topic | Sub-topic |
| Introduction to optimization algorithms | General scheme of evolutionary algorithms. Basic concepts: search domain, constraints, penalties. No Free Lunch theorem. Basic concepts of multi-objective optimization. |
| Paradigms and meta-heuristics of nature-inspired algorithms | Bio-inspired metaheuristics. Swarm intelligence. |
| Specific algorithms of evolutionary computation | Genetic algorithms. Evolutionary strategies. Genetic programming. Examples of swarm intelligence: Particle Swarm Optimization, Arficial Bee Algorithm, Bacterial Colony Optimization, Ant algorithms. Examples of other bio-inspired evolutionary algorithms. |
| Advances in automatic adaptation of evolutionary algorithms | Automatic adaptation of the defining parameters of an EA. Use of hyper-heuristics. |
|
