| Study programme competencies |
| Code | Study programme competences |
| A11 | CE10 - Ability to implement, validate and apply a stochastic model starting from the observed data on a real system, and to perform a critical analysis of the obtained results, selecting those ones most suitable for problem solving |
| A12 | CE11 - Understanding and command of the main techniques and tools for data analysis, both from the statistical and the machine learning viewpoints, including those devised for large volumes of data, and ability to select those ones most suitable for problem solving |
| A13 | CE12 - Ability to outline, formulate and solve all the stages of a data project, including the understanding and command of basic concepts and techniques for information search and filtering in big collections of data |
| A16 | CE15 - Knowledge of computer tools in the field of machine learning and ability to select those ones most suitable for problem solving |
| B2 | CG02 - Successfully addressing each and every stage of an AI project |
| B3 | CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field |
| B4 | CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field |
| B5 | CG05 - Working in teams, especially of multidisciplinary nature, and being skilled in the management of time, people and decision making |
| B6 | CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
| B7 | CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
| B8 | CB03 - The students will be able to integrate different pieces of knowledge, to face the complexity of formulating opinions (from information that may be incomplete or limited) and to include considerations about social and ethical responsibilities linked to the application of their knowledge and opinions |
| B9 | CB04 - The students will be able to communicate their conclusions, their premises and their ultimate justifications, both to specialised and non-specialised audiences, using a clear style language, free from ambiguities |
| C3 | CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life |
| C4 | CT04 - Acquiring a personal development for practicing a citizenship under observation of the democratic culture, the human rights and the gender perspective |
| C7 | CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development |
| C8 | CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
| C9 | CT09 - Being able to manage time and resources: outlining plans, prioritising activities, identifying criticisms, fixing deadlines and sticking to them |
| Learning aims | |||
| 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 |
| Contents |
| 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. |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | 10.5 | 10.5 | 21 |
| Objective test | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | 3 | 0 | 3 |
| Laboratory practice | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | 10.5 | 31.5 | 42 |
| Mixed objective/subjective test | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | 2 | 2 | 4 |
| Personalized attention | 5 | 0 | 5 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Guest lecture / keynote speech | Oral presentation of the theory topics by the professors of the course. |
| Objective test | Test/exam of the concepts explained in theory classes. |
| Laboratory practice | Laboratory sessions in which the necessary concepts will be explained in order to carry out programming practices related to optimization problems with evolutionary algorithms. The professors will indicate which optimization problems will be considered, as well as the programming platform/language to be used in the use or implementation of different evolutionary/bio-inspired algorithms. The professors will indicate whether this work will be carried out by the students autonomously or in groups, and their progress will be supervised by the teachers. |
| Mixed objective/subjective test | Continuous monitoring of the practices carried out, by means of class attendance and continuous and final correction of the same. The possibility of a brief oral presentation of the work done in this part is included. |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Guest lecture / keynote speech | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | The theoretical part of the course will be continuously monitored through class attendance and possible test-type questionnaires at the end of the lectures. | 5 |
| Laboratory practice | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | Evaluation of the different practices carried out by the students. | 50 |
| Objective test | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | Final exam of the theoretical part. | 40 |
| Mixed objective/subjective test | A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C4 C7 C8 C9 | There will be a continuous monitoring of the practices carried out, by means of class attendance and continuous and final correction of the same. The possibility of a brief oral presentation of the work done in this part is included. | 5 |
| Assessment comments | |||
| Sources of information |
| Basic |
Dan Simon (2013). Evolutionary Optimization Algorithms. Wiley
A. E. Eiben (2010). Introduction to Evolutionary Computing (Natural Computing Series). Springer |
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| Recommendations | |
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus |
| Other comments | |