Checking date: 30/05/2022 11:27:13

Versión en español

Course: 2022/2023

Evolutionary Computation
(19202)
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)
EPI


Coordinating teacher: SAEZ ACHAERANDIO, YAGO

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS

Course: 1º
Semester: 1º



Requirements (Subjects that are assumed to be known)
Average programming skills
Objectives
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).
Learning Outcomes
Link to document

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. Mathematical foundations
Learning activities and methodology
Lectures Practice sessions Tutorship Team work Individual student work Presentations for partial and final assessments
Assessment System
  • % end-of-term-examination/test 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.