Course detail
Evolutionary Computation
FIT-EVDAcad. year: 2021/2022
Evolutionary computation in the context of artificial intelligence and hard optimization problems. Single- and multi-objective optimization, dominance relation, Pareto front. Principles of genetic algorithms, evolutionary strategy, genetic programming and other evolutionary heuristics. Statistical evaluation, theoretical analysis of evolutionary algorithms. Advanced evolutionary algorithms based on probabilistic models. Parallel evolutionary algorithms. Multi-objective evolutionary algorithms. Rapid prototyping of evolutionary algorithms.
Doctoral state exam - topics:
- Problem encoding, genotype, phenotype, fitness function.
- Genetic algorithms, schema theory.
- Evolution strategies.
- Genetic programming and symbolic regression.
- Estimation distribution algorithms.
- Simulated annealing
- Multi-objective evolutionary optimization.
- Parallel evolutionary algorithms.
- Differential evolution, SOMA.
- Statistical analysis of experiments.
Language of instruction
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Deeper understanding of the optimization problem and its solution in computer engineering.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer, 2015, ISBN 978-3-662-43630-1.
Doerr, B. Neumann F. (eds.): Theory of Evolutionary Computation. Springer, 2020, ISBN 978-3-030-29413-7
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. 2nd ed. Springer, 2015, ISBN 978-3-662-44873-1.
Kvasnička V., Pospíchal J., Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5.
Classification of course in study plans
- Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
- Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, summer semester, elective
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, summer semester, elective
- Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional
- Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, summer semester, elective
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, summer semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction to evolutionary computation.
- Genetic algorithms, schema theory.
- Statistical analysis of experiments.
- Typical optimization problems.
- Advanced techniques in genetic algorithms.
- Theoretical analysis of evolutionary algorithms.
- Multi-objective evolutionary optimization.
- Evolution strategies.
- Genetic programming and symbolic regression.
- Parallel evolutionary algorithms.
- Estimation distribution algorithms.
- Simulated annealing, differential evolution, SOMA and other relevant algorithms.
- Recent trends.
Guided consultation in combined form of studies
Teacher / Lecturer