ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 11 Total: 22
Use languages Spanish, Galician, English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: External department linked to the degrees
Areas: Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The course introduces the student to the modeling of systems capable of adapting to
their environments and learning from their experience, imitating the evolutionary
processes of nature. In this context, the student will be instructed not only in the use of
different techniques for the search of solutions inspired by the prevalence or subsistence
strategies of a population, but also in the application of meta-heuristics for their
optimization
Introduction to optimization algorithms. Paradigms and meta-heuristics of algorithms inspired by nature. Algorithms specific to evolutionary computation. Advances in automatic adaptation of evolutionary algorithms.
Basic:
Dan Simon, Evolutionary Optimization Algorithms., 978-0-470-93741-9, Wiley, 2013
E. Eiben, Introduction to Evolutionary Computing (Natural Computing Series), 978-3-662-44874-8, Springer, 2010
Complementary:
Blog con ejemplos prácticos sobre computación evolutiva escritos en Java y de licencia
GPL.
https://web.archive.org/web/20121013005352/http://algoritmoevolutivo.bl…
011/10/computacion-evolutiva-ejemplo-i.htm. Recuperado el 12/05/2022.
BASIC AND GENERAL
GC2 - Successfully tackle all the stages of an Artificial Intelligence project.
GC3 - Search and select useful information needed to solve complex problems,
handling with fluency the bibliographic sources of the field.
GC4 - Elaborate adequately and with some originality written compositions or
motivated arguments, write plans, work projects, scientific articles and formulate
reasonable hypotheses in the field.
CG5 - Work in teams, especially multidisciplinary teams, and be skilled in time
management, people and decision making.
CB6 - Possess and understand knowledge that provides a basis or opportunity for
originality in the development and/or application of ideas, often in a research context.
CB7 - That students know how to apply acquired knowledge and problem-solving skills
in new or unfamiliar environments within broader (or multidisciplinary) contexts related
to their area of study.
CB8 - That students are able to integrate knowledge and face the complexity of making
judgments based on information that, being incomplete or limited, includes reflections
on the social and ethical responsibilities linked to the application of their knowledge and
judgments.
TRANSVERSALS
CT3 - Use the basic tools of information and communication technologies (ICT)
necessary for the exercise of their profession and for lifelong learning.
CT7 - Develop the ability to work in interdisciplinary or transdisciplinary teams, to
offer proposals that contribute to sustainable environmental, economic, political and
social development.
CT8 - Value the importance of research, innovation and technological development in
the socioeconomic and cultural progress of society.
CT9 - Have the ability to manage time and resources: develop plans, prioritize
activities, identify critical ones, set deadlines and meet them.
SPECIFIC
CE10 - Ability to build, validate and apply a stochastic model of a real system from
observed data and critical analysis of the results obtained.
SC11 - Understanding and mastery of the main techniques and tools of data analysis,
both from the statistical point of view and machine learning, including those dedicated
to the treatment of large volumes of data, and ability to select the most appropriate for
problem solving.
CE12 - Ability to plan, formulate and solve all stages of a data project, including
understanding and mastery of basic fundamentals and techniques for searching and
filtering information in large data collections.
CE15 - Knowledge of computer tools in the field of machine learning, and ability to
select the most appropriate for solving a problem.
The methodology includes the expository method / lecture, laboratory practices,
tutorials, independent work, case studies, project-based learning. It will be carried out
with the following training activities:
1) Problem-based learning, seminars, case studies and projects: these are sessions whose
objective is that students acquire certain skills based on the resolution of exercises, case
studies and projects that require the student to apply the knowledge and skills developed
during the course. These sessions may require the student to present orally the solution
to the problems posed. The work carried out by the students can be done individually or
in work groups.
2) Theory classes: Oral exposition complemented with the use of audiovisual media and
the introduction of some questions directed to the students, with the purpose of
transmitting knowledge and facilitating learning. In addition to the time of oral
exposition by the professor, this formative activity requires the student to dedicate some
time to prepare and review on their own the materials object of the class.
3) Practical laboratory classes: classes dedicated to the development of practical work
involving the resolution of complex problems, and the analysis and design of solutions
that constitute a means for their resolution. This activity may require students to present
their work orally. The work done by the students can be done individually or in work
groups
The assessment will consist of three parts:
- An exam of objective questions, with a weighting of 40% of the final grade. Different evaluation tests will be carried out at the end of the course, especially oriented to evaluate the understanding of the knowledge exposed in the theory and practical classes.
- Assessment of practical work, with a weighting of 50% of the final grade: the solutions proposed by the students to the exposed practices will be evaluated. The evaluation of practical work will be carried out by means of a correction by the teacher, a defense of the solution provided by the student in a report or an oral presentation of the solution developed.
- Continuous monitoring of the laboratory practices, with a weighting of 5% of the final grade: it is the part of the evaluation of the students that is based on a continuous monitoring of their evolution and work within the framework of the subject based on the participation in the training activities.
- Continuous monitoring of the master class, with a weighting of 5% of the final grade: this is the part of the students' evaluation based on a continuous monitoring of their evolution and work within the framework of the subject based on their participation in the training activities.
The final grade will be the sum of all the aforementioned sections and the subject will be approved if such sum is greater or equal to 5.
All students who perform any of the laboratory practices or any practical work are understood to accept the continuous evaluation procedure described above.
The evaluation criteria for the 2nd opportunity and for students without continuous evaluation (in both opportunities) will be the following:
-Final written exam that will cover all the content of the subject, with weighting of 100% of the final grade.
A1: Theory classes: 10 classroom hours, 20 hours total dedication.
A2: Practical laboratory classes: 7 classroom hours, 28 hours total dedication.
A3: Problem-based learning, seminars, case studies and projects: 4 classroom hours, 27
hours total dedication
This course is offered by the University of Vigo.
| Wednesday | |||
|---|---|---|---|
| 15:30-17:00 | Grupo /CLE_01 | English | IA.02 |
| 05.27.2025 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 05.27.2025 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 07.02.2025 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 07.02.2025 16:00-20:00 | Grupo /CLIL_01 | IA.02 |