Checking date: 27/04/2024

Course: 2024/2025

Evolutionary Computation
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

Coordinating teacher: SAEZ ACHAERANDIO, YAGO

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Average programming skills
Understand the fundamentals of evolutionary computing, be able to identify in which cases it can be effective and acquire the knowledge to choose and design the appropriate technique to a given problem, commonly, search and optimization problems (among others).
Skills and learning outcomes
Description of contents: programme
1. Introduction to evolutionary computation 2. General concepts of evolutionary algorithms: initialization, stop, genetic operators, insertion and replacement strategies. 3. Evolutionary computation techniques: genetic algorithms, evolutionary strategies, genetic programming, others. 4. Problem solving through evolutionary techniques. Problems with multiple solutions, with several objectives, with restrictions, coevolution. 5. Alternative systems: applications and examples of solving complex problems
Learning activities and methodology
Formation activities AF1 - Theoretical class AF2 - Practical classes AF3 - Theoretical and practical classes AF5 - Tutorials AF6 - Group work AF7 - Individual student work AF8 - Partial and final exams -> Presentations and/or partial and final dissertations teaching methodology MD1 - Presentations in the teacher's class with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the learning of the students. MD2 Critical reading of texts recommended by the professor of the subject: articles, reports, videos, tutorials, etc., either for later discussion in class, or to broaden and consolidate knowledge of the subject. MD3 Resolution of practical cases, problems, etc. raised by the teacher individually or in groups MD5 Preparation of work and reports individually or in groups
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100

Calendar of Continuous assessment

Basic Bibliography
  • D. Floreano, C. Mattiussi. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press. 2008
  • E. Talbi. Metaheuristics: From Design to Implementation. Wiley. 2009

The course syllabus may change due academic events or other reasons.