Checking date: 23/07/2025 12:16:03


Course: 2025/2026

Bio-inspired methods
(20608)
Bachelor in Computer Science and Engineering (Plan: 570 - Estudio: 218)


Coordinating teacher:

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Learning Outcomes
K5: Knowledge and application of the basic algorithmic procedures of computer technologies to design solutions to problems, analyzing the suitability and complexity of the proposed algorithms. K12: Knowledge and application of the fundamental principles and basic techniques of intelligent systems and their practical application. KOPT_1: To know and understand in depth advanced technologies in a specific area related to computer engineering, which constitute the state of the art in their area of study, including emerging trends and recent developments. KOPT_2: To interpret scientific and technical information sources to deepen knowledge in a specific area related to computer engineering. S2: Ability to understand the fundamentals, paradigms and techniques of intelligent systems and to analyze, design and build systems, services and computer applications that use these techniques in any field of application. SOPT_1: To identify, assess their technical feasibility, and apply advanced tools, methodologies, and technological solutions used in the degree field, in order to develop algorithms or systems that integrate cutting_edge and innovative technologies. SOPT_2: To apply analytical and design methodologies to solve advanced problems in the field of computer engineering, and evaluate the performance and limitations of different technological approaches, proposing improvements and alternatives. COPT_1: To conceive and develop projects that integrate advanced knowledge and provide innovative solutions in the field of study of computer engineering.
Description of contents: programme
1. Introduction to bio-inspired computing techniques 1.1. Biological Inspiration in Engineering 1.2. General concepts of evolutionary algorithms 2. Evolutionary computing systems 2.1. Genetic algorithms 2.2. Genetic programming 2.3. Evolutionary strategies 2.4. Genetic expressions 3. Problem solving 3.1. Multiple solutions 3.2. Multiple objectives 3.3. Constraints 4. Parallel methods 4.1. Master-slave model 4.2. Island models 5. Swarm systems 5.1. Particle swarms 5.2. Ant Colony Optimization 6. Applications of bio-inspired computational techniques 6.1. Optimization 6.2. Classification, clustering, data analysis 6.3. Hybrid and interactive systems
Assessment System

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