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.
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Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
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.
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Syllabus - others, projects and individual work of students:
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
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Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans
- Programme IT-MSC-2 Master's
branch MMI , 0 year of study, summer semester, elective
branch MBI , 0 year of study, summer semester, compulsory-optional
branch MSK , 0 year of study, summer semester, elective
branch MMM , 0 year of study, summer semester, elective
branch MBS , 0 year of study, summer semester, elective
branch MIS , 0 year of study, summer semester, elective
branch MIN , 0 year of study, summer semester, elective
branch MGM , 0 year of study, summer semester, elective
branch MPV , 0 year of study, summer semester, compulsory-optional
Type of course unit
Lecture
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.