Course detail
Evolutionary Computation
FIT-EVDAcad. year: 2017/2018
Evolutionary computation in the context of artificial intelligence and optimization problems with NP complexity. Paradigm of genetic algorithms, evolutionary strategy, genetic programming and another evolutionary heuristics. Theory and practice of standard evolutionary computation. Advanced evolutionary algorithms based on graphic probabilistic models (EDA - estimation of distribution algorithms). Parallel evolutionary algorithms. A survey of representative applications of evolutionary algorithms in multi-objection optimization problems, artificial intelligence, knowledge based systems and digital circuit design. Techniques of rapid prototyping of evolutionary algorithms.
Language of instruction
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
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.
- Genetic algorithms (GA), schemata theory.
- Advanced genetic algorithms
- Repesentative combinatorial optimization problems.
- Evolution strategies.
- Genetic programming.
- Advanced estimation distribution algorithms (EDA).
- Variants of EDA algorithms, UMDA, BMDA and BOA.
- Simulated annealing.
- Methods for multicriterial and multimodal problems. Selection and population replacement.
- Techniques for fast prototyping. Structure of development systems and GA library.
- New evolutionary paradigm: immune systems, differential evolution, SOMA.
- Typical application tasks.
- Defence of a project, software project based on a variant of evolutionary algorithm
Syllabus - others, projects and individual work of students:
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
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Evolutionary algorithms, theoretical foundation, basic distribution.
- Genetic algorithms (GA), schemata theory.
- Advanced genetic algorithms
- Repesentative combinatorial optimization problems.
- Evolution strategies.
- Genetic programming.
- Advanced estimation distribution algorithms (EDA).
- Variants of EDA algorithms, UMDA, BMDA and BOA.
- Simulated annealing.
- Methods for multicriterial and multimodal problems. Selection and population replacement.
- Techniques for fast prototyping. Structure of development systems and GA library.
- New evolutionary paradigm: immune systems, differential evolution, SOMA.
- Typical application tasks.