460-6030/02 – Bio-inspired Algorithms (BIOA)
| Gurantor department | Department of Computer Science | Credits | 10 |
| Subject guarantor | doc. Ing. Petr Gajdoš, Ph.D. | Subject version guarantor | doc. Ing. Petr Gajdoš, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The aim of the course is to provide students a deeper overview of implementation and usage of bio-inspired algorithms. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of its Ph.D. studies and dissertation work.
Teaching methods
Seminars
Individual consultations
Project work
Other activities
Summary
This course provides the students with working knowledge of bio-inspired algorithms and their applications. It introduces the basic concepts of bio-inspired methods, briefly discusses their history and concentrates on the current state and recent developments in this field. The course first outlines the fundamental concepts of bio-inspired computation as such and then discusses the basic categories of bio-inspired methods including evolutionary computation, swarm intelligence, artificial neural networks, and hybrid methods. The students are also familiarized with different types of problems, typically solved by bio-inspired methods. In particular, continuous and discrete problems are discussed and bio-inspired methods, suitable for different types of problems, are discussed. Last but not least, the methods and techniques for the statistical evaluation and visualization of the results of bio-inspired algorithms are discussed.
The languages and frameworks for the practical design and implementation of bio-inspired methods in the scope of the course will include Python (scikit-learn), C/C++, and R (caret package).
Compulsory literature:
• M. Affenzeller, S. Winkler, S. Wagner, A. Beham, Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications, Chapman & Hall/CRC, 2009.
• C. Blum, D. Merkle, Swarm Intelligence: Introduction and Applications, Springer Publishing Company, Incorporated, 2008.
• M. Clerc, Particle Swarm Optimization, ISTE, Wiley, 2010.
• M. Dorigo, T. Stützle, Ant Colony Optimization, MIT Press, Cambridge, MA, 2004.
• A. Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley, New York, NY, USA, 2005.
Recommended literature:
• A. Engelbrecht, Computational Intelligence: An Introduction, 2nd Edition, Wiley, New York, NY, USA, 2007.
Additional study materials
Way of continuous check of knowledge in the course of semester
Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.
Oral exam.
E-learning
Other requirements
The student prepares and presents the work on a given topic.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
• Bio-inspired computation: problem representation, emulation of biological principles. Candidate solutions, fitness, and survival of the fittest. Exploration and exploitation.
• Trajectory and population-based methods, families of bio-inspired methods: evolutionary computation, swarm intelligence, artificial neural networks.
• Evolutionary computation: basic principles (population, selection, elimination, ...), genetic algorithms, genetic programming, differential evolution.
• Swarm intelligence: basic principles (social intelligence), particle swarm optimization, ant colony optimization, artificial bee colony optimization.
• Artificial neural networks: artificial neuron, multilayer networks, deep networks. Supervised and unsupervised learning, deep learning.
• Continuous problems, parameter learning, benchmarking functions.
• Combinatorial optimization problems, permutation (travelling salesman problem) and subset selection problems (feature subset selection).
• Statistical analysis, evaluation, and visualization of bio-inspired methods.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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