Course: Evolutionary Computation Techniques

» List of faculties » FAI » AUIUI
Course title Evolutionary Computation Techniques
Course code AUIUI/AEEVT
Organizational form of instruction Lecture + Tutorial
Level of course Master
Year of study not specified
Semester Winter and summer
Number of ECTS credits 6
Language of instruction English
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Pluháček Michal, doc. Ing. Ph.D.
  • Zelinka Ivan, prof. Ing. Ph.D.
  • Komínková Oplatková Zuzana, prof. Ing. Ph.D.
  • Šenkeřík Roman, prof. Ing. Ph.D.
Course content
- The current state of the field softcomputing, fuzzy logic, neural networks, evolutionary computing (EVT). Classification of evolutionary computation, historical facts, current trends in EVT. The central dogma of EVT under Darwin and Mendel. - No Free Lunch Theorem. Computational complexity of algorithms and physical limits. Multipurpose optimization and Pareto set. - Restrictions placed on the utility function and individual parameters. Penalties and its impact on the geometry of the objective function. Working with real, integer and discrete values ??of individual parameters. Genetic algorithms. GA terminology. The principle of operation, Hybrid GA, messy GA, parallel GA, migration and diffusion models. - Evolutionary Strategy. No-man - EC, Multi-EC, Adaptive EC. Swarming particles (Particle Swarm). Search suspended (Scatter Search). Ant colony optimization (Ant Colony Optimization). - SOMA: Self-Organizing Migrating Algorithm operating principles and strategies used by the algorithm: ATO, ATR ATAA and ATA. Differential evolution, principles of operation and used versions: DE/best/1/exp, DE/rand/1/exp, DE/rand-to-best/1/exp, DE/best/2 / exp DE/rand/2 / exp DE/best/1/bin, DE/rand/1/bin, DE/rand-to-best/1/bin, DE/best/2/bin, DE/rand/2/bin. SOMA, DE and permutation test problems. - Genetic programming techniques: genetic programming, grammatical evolution. Alternatives: analytical programming, Probabilistic Incremental Program Evolution - PIPE, Gene Expression Programming, Programming Multiexpression and more. - Evolutionary Hardware (EH). Inspiration in biology. Computing devices. Reconfigurable devices. Evolutionary design and digital circuits. EH and cellular automata. Polymorphic electronics. - Cellular Automata (BA) and complex systems. Introduction, BA formalism, dynamics and classification according to Wolfram's cellular automata, Conway's Game of Life, using BA modeling. - Artificial life. Basic definitions and existing systems and models. Tierra, biomorfové, Sbeat, Sbart Eden, Galapagos ... Self-reproducing automata according to Turing and von Neumann. Langton's loop, computer viruses and artificial life. Artificial life and the edge of chaos (by Kaufmann) - Neural Networks (ANN). History and basic principles of NS. The training set and its use of NS. The basic types of networks and their applications to different types of problems. - Fractal geometry. History, definition of fractal, basic types of algorithms that generate fractals. Fractal dimension, interpolation and compression. Developmental systems and artificial life. L-systems, turtle graphics, parametric L-systems, L-systems from the perspective of fractal geometry. - Immunological systems (IS). The principle of the IS, the IS limits, algorithms implementing IS imunotronika. - Swarm Intelligence (SI). Basic concepts and definitions, representative algorithms SI - Particle Swarm, scatter search, ant colony optimization, swarm robotic, artificial evolution of complex systems. - DNA computing. DNA computing as part of bioinformatics, DNA and the binary representation according Adlemanna. Watson Crickův machine. Mathematical modeling of DNA operations.

Learning activities and teaching methods
Lecturing
prerequisite
Knowledge
Knowledge from areas: Mathematics Fundamentals of Informatics AI
Knowledge from areas: Mathematics Fundamentals of Informatics AI
learning outcomes
The student can define and describe the basic concepts of ECT, its history and current trends, and understands parallels with biological processes and the classification of ECT.
The student can define and describe the basic concepts of ECT, its history and current trends, and understands parallels with biological processes and the classification of ECT.
The student has knowledge about benchmarking algorithms, population creation, soft and hard constraints, and understands different types of individual encoding.
The student has knowledge about benchmarking algorithms, population creation, soft and hard constraints, and understands different types of individual encoding.
The student understands the differences between single-member and multi-member evolutionary strategies, CMAES, genetic algorithms, differential evolution, and swarm algorithms.
The student understands the differences between single-member and multi-member evolutionary strategies, CMAES, genetic algorithms, differential evolution, and swarm algorithms.
The student can define and explain swarm algorithms like PSO, SOMA, Ant Colony Optimization, and Firefly algorithm, and understands hybrid strategies.
The student can define and explain swarm algorithms like PSO, SOMA, Ant Colony Optimization, and Firefly algorithm, and understands hybrid strategies.
The student can clarify the principles of evolutionary symbolic regression, genetic programming, and analytical programming.
The student can clarify the principles of evolutionary symbolic regression, genetic programming, and analytical programming.
Skills
The student can design and implement evolutionary strategies to solve specific problems.
The student can design and implement evolutionary strategies to solve specific problems.
The student can apply and optimize various types of evolutionary algorithms for specific purposes such as optimization and approximation.
The student can apply and optimize various types of evolutionary algorithms for specific purposes such as optimization and approximation.
The student has skills in benchmarking and tuning evolutionary and swarm algorithms to improve their performance.
The student has skills in benchmarking and tuning evolutionary and swarm algorithms to improve their performance.
The student can effectively use swarm algorithms to solve practical problems.
The student can effectively use swarm algorithms to solve practical problems.
The student is capable of applying evolutionary computing techniques in various interdisciplinary real-world applications.
The student is capable of applying evolutionary computing techniques in various interdisciplinary real-world applications.
teaching methods
Knowledge
Lecturing
Lecturing
assessment methods
Oral examination
Oral examination
Recommended literature
  • Koza, J. R. Genetic Programming. Cambridge : MIT Press, 1998. ISBN 0-262-11189-6.
  • Koza, John R. Genetic Programming : Darwinian Invention and Problem Solving. San Francisco : Morgan Kaufmann Publishers, 1999. ISBN 1558605436.
  • Mařík V. Štěpánková O., Lažanský J. Umělá inteligence IV. Academia, Praha, 2004. ISBN 80-200-1044-0.
  • Mařík, Vladimír. Umělá inteligence. Vyd. 1. Praha : Academia, 2001. ISBN 8020004726.
  • Novák, V. Fuzzy množiny a jejich aplikace. Praha : SNTL, 1990. ISBN 80-03-00325-3.
  • Pokorný, Miroslav. Řídící systémy se znalostní bází. Dotisk 1. vyd. Ostrava : VŠB, 1999. ISBN 8070782757.
  • Posíchal, Jiří. Evolučné algoritmy. 1. vyd. Bratislava : STU, 2000. ISBN 8022713775.
  • Šnorek, M., Jiřina, M. Neuronové sítě a neuropočítače. Praha : ČVUT, 1996. ISBN 80-01-01455-X.
  • Vysoký, Petr. Fuzzy řízení. Vyd. 1. Praha : Vydavatelství ČVUT, 1996. ISBN 80-01-01429-8.
  • Zelinka I., Oplatkova´ Z., Šeda M., Ošmera P., Včelař F. Evoluční vy´početní techniky, principy a aplikace. BEN, 2008.
  • Zelinka, I. Umělá inteligence II.
  • Zelinka, Ivan. Umělá inteligence : neuronové sítě a genetické algoritmy. 1. vyd. Brno : VUTIUM, 1998. ISBN 8021411635.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester