Course detail

Bio-inspired Computing

FSI-VBCAcad. year: 2024/2025

The course introduces basic and advanced methods of so called biology inspired computing. Focus is on practical implementation of this special class of artificial intelligence algorithms. Usability of the methods is demonstrated with mathematical and engineering problems.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

prof. Ing. Radomil Matoušek, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

Entry knowledge

Statistics and Optimization Methods I.

Rules for evaluation and completion of the course

Requirements for credit: Students will be divided into teams. They must submit 4 functioning software projects for each team. Each team member must be able to present and understand the projects. Concrete specification will be on the laboratory exercise. Consultations are provided and project progress is checked continuously. Individual projects are in completion. Maximum points form exercises is 100, credit limit is 60.
Attendance at seminars is controlled. An absence can be compensated for via solving additional problems.

Aims

Goal of the course is to introduce students to modern tools of biology inspired computing and options and appropriate usage for solving engineering tasks.
Knowledge: Students will know basic principles and algorithms of presented methods usable in continuous and combinatorial optimization and their options, restrictions and potential for implementation.
Skills: Ability to use these methods to solve practical engineering problems where methods of mathematical optimization may not provide acceptable results.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

KVASNIČKA, Vladimír. Evolučné algoritmy. Bratislava: Vydavateľstvo STU, 2000. Edícia vysokoškolských učebníc. ISBN isbn80-227-1377-5. (SK)
ZELINKA, Ivan. a kol. Evoluční výpočetní techniky. Principy a aplikace. Praha, BEN 2009. (CS)

Recommended reading

DORIGO, M., STüTZLE, T. Ant Colony Optimization. MIT Press 2004. (EN)
HAUPT, R. L., HAUPT, S. E. Practical Genetic Algorithms. John Wiley & Sons 1998. (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme N-AIŘ-P Master's 2 year of study, winter semester, compulsory

  • Programme C-AKR-P Lifelong learning

    specialization CZS , 1 year of study, winter semester, elective

Type of course unit

 

Lecture

13 hod., optionally

Teacher / Lecturer

prof. Ing. Radomil Matoušek, Ph.D.

Syllabus

B1: Biology inspired computation - introduction. History and division of evolutionary computing techniques (ECT). Standard genetic algorithms (SGA). Holland's schema theorem. Building Block Hypothesis.
B2: Advanced GA. Problem coding methods. Combinatorial optimization using GA. 4. Grammar Evolution (GE). Genetic Programming (GP). Symbolic regression tasks. Cartesial Genetic Programming (CGA). Evolutionary design of combinational logic circuits.
B3: Evolution Strategy (ES). Differential Evolution (DE). Representation. Basic models. Binary string searching algorithm HC12. Nelder-Mead algorithm. Algorithms using patterns. Bayesian optimization algorithms.
B4: Swarm algorithms I. (Ant Colony strategy, Bee Colony Optimization). Swarm algorithms II. (Particle Swarm Optimization, Firefly algorithm, SOMA).
B5: Cellular automata I – theory basics. Cellular automata II – practical applications.
B6: Summary – colloquium.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Tomáš Hůlka, Ph.D.

Syllabus

Teaching will be divided into 4 blocks reflecting real usage of biology inspired computation. Students will work in groups and compare in competition the obtained results.
A. Implementation of GA and solution of concrete optimization task*
B. Implementation of chosen meta-heuristics and solution of concrete optimization task *
C. Implementation of CGA for evolutionary design of hardware
D. Implementation of Cellular automata
*Tasks of combinatorial, integer and mixed optimization (TSP, QAP, controller design, symbolic regression, global optimization of multi-modal functions, etc.)

Elearning

eLearning: currently opened course