Course detail
Applied Evolutionary Algorithms
FIT-EVOAcad. year: 2017/2018
Multiobjective optimization problems, standard approaches and stochastic evolutionary algorithms (EA), simulated annealing (SA). Evolution strategies (ES) and genetic algorithms (GA). Tools for fast prototyping. Representation of problems by graph models. Evolutionary algorithms in engineering applications namely in synthesis and physical design of digital circuits, artificial intelligence, signal processing, scheduling in multiprocessor systems and in business commercial applications.
Supervisor
Department
Learning outcomes of the course unit
Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of methodology of fast prototyping of evolutionary optimizer utilizing GA library and present design tools.
Prerequisites
There are no prerequisites
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
- Kvasnička V., Pospíchal J., Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5
- Kvasnička V., a kol.: Úvod do teorie neuronových sítí, Iris 1997, ISBN 80-88778-30-1.
- Dasgupta D., Michalewicz Z.: Evolutionary algorithms in engineering applications. Springer Verlag, Berlin, 1997, ISBN 3-540-62021-4.
- Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
- Kvasnička V., Pospíchal J.,Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5.
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.
Language of instruction
Czech
Work placements
Not applicable.
Course curriculum
- Syllabus of lectures:
- Evolutionary algorithms, theoretical foundation, basic distribution (GA, EP,GP, ES).
- Genetic algorithms (GA), schemata theory.
- Genetic algorithms using diploids and messy-chromosomes. Specific crossing.
- Representative combinatorial optimization problems.
- Evolutionary programming, Hill climbing algorithm, Simulated annealing.
- Genetic programming.
- Advanced estimation distribution algorithms (EDA).
- Variants of EDA algorithms, UMDA, BMDA and BOA.
- Multimodal and multi-criterial optimization.
- Dynamic optimization problems.
- New evolutionary paradigm: immune systems, differential evolution, SOMA.
- Differential evolution. Particle swarm model.
- Engineering tasks and evolutionary algorithms.
- Simple design of an optimizer with GADesign system.
- Utilizing of GA libraries like GAlib.
- Genetic programming in Java.
- Illustration of the program BMDA.
- Implementation of a given application from the field of evolutionary computation or
- study of a given paper, presentation of main ideas.
Syllabus of laboratory exercises:
Syllabus - others, projects and individual work of students:
Aims
Survey about actual optimization techniques and evolutionary algorithms for solution of complex, NP complete problems. To make students familiar with software tools for fast prototyping of evolutionary algorithms and learn how to solve typical complex tasks from engineering practice.
Specification of controlled education, way of implementation and compensation for absences
Midterm and final test, one project.
Classification of course in study plans
- Programme IT-MGR-2 Master's
branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
branch MGM , any year of study, summer semester, 5 credits, elective
branch MSK , any year of study, summer semester, 5 credits, elective
branch MIS , any year of study, summer semester, 5 credits, elective
branch MBS , any year of study, summer semester, 5 credits, elective
branch MIN , any year of study, summer semester, 5 credits, elective
branch MMI , any year of study, summer semester, 5 credits, elective
branch MMM , any year of study, summer semester, 5 credits, elective
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
- Evolutionary algorithms, theoretical foundation, basic distribution (GA, EP,GP, ES).
- Genetic algorithms (GA), schemata theory.
- Genetic algorithms using diploids and messy-chromosomes. Specific crossing.
- Representative combinatorial optimization problems.
- Evolutionary programming, Hill climbing algorithm, Simulated annealing.
- Genetic programming.
- Advanced estimation distribution algorithms (EDA).
- Variants of EDA algorithms, UMDA, BMDA and BOA.
- Multimodal and multi-criterial optimization.
- Dynamic optimization problems.
- New evolutionary paradigm: immune systems, differential evolution, SOMA.
- Differential evolution. Particle swarm model.
- Engineering tasks and evolutionary algorithms.