* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code | 460-4086/02 | |||||
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Number of ECTS Credits Allocated | 4 ECTS credits | |||||

Type of Course Unit * | Optional | |||||

Level of Course Unit * | Second Cycle | |||||

Year of Study * | ||||||

Semester when the Course Unit is delivered | Winter Semester | |||||

Mode of Delivery | Face-to-face | |||||

Language of Instruction | Czech, English | |||||

Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester | |||||

Name of Lecturer(s) | Personal ID | Name | ||||

ZEL01 | prof. Ing. Ivan Zelinka, Ph.D. | |||||

SKA206 | Ing. Lenka Skanderová, Ph.D. | |||||

Summary | ||||||

The course will discuss a wider range of evolutionary computation. They
mentioned as historically classic techniques and modern algorithms. There will also be discussed at the introductory level, cellular automata, artificial life, neural networks, evolutionary hardware, DNA computing, etc. Great emphasis will be placed on the practical side of things - the ability to most discussed methods applied to practical examples. Students should have the comprehensive knowledge of the course of the above areas, including the possibility its use. The course includes laboratory exercises in which students will practice how to program the selected algorithms and their application to solving practical problems. | ||||||

Learning Outcomes of the Course Unit | ||||||

The goal is to introduce the students with modern methods of calculation derived from evolutionary and biological processes (evolutionary algorithms, cellular automata etc.). Student will gain an overview of modern computer-based procedures principles of observation of biological processes and dynamics. Upon successful completion of graduate course will be able to apply the methods discussed in the course to real problems. | ||||||

Course Contents | ||||||

Lectures:
1.Evolutionary algorithms 1. 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, according to Darwin, and EVT Mendel. 2. Evolutionary Algorithms 2. No Free Lunch Theorem. Computational complexity and physical limitations algorithms. Multipurpose optimization and Pareto set. 3. Evolutionary algorithms 3. 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. Principle activities, Hybrid GA, messy GA, parallel GA, migration and diffusion models. 4. Evolutionary algorithms 4. Evolutionary Strategy. No-man (1 +1)-EC.Multi-EC (μ + Λ)-ES and (μ, λ)-ES. Multi-EC (μ + λ)-ES and (μ, λ)-ES. Adaptive EC.Particle swarm (Particle Swarm). Search suspended (Scatter Search). Ant colony optimization (ant Colony Optimization). 5. Evoluční algorithms 5. SOMA: Self-Organizing Migrating Algorithm principle of operation and strategies used by the algorithm: ATO, ATR ATAA and ATA. Differential evolution principle activities 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. 6. Evolutionary algorithms 6. Techniques of Genetic Programming: Genetic Programming, grammatical evolution. Alternatives: analytical programming, Probabilistic Incremental Program Evolution - PIPE, Gene Expression Programming, Programming Multiexpression and more. 7. Evolutionary Hardware (EH). Inspiration in biology. Computing devices.Reconfigurable equipment. Evolutionary design and digital circuits. EH and cellular automata. Polymorphous electronics. 8. Cellular Automata (BA) and complex systems. Introduction, Formalism BA Dynamics and classification according to Wolfram's cellular automata, Conway's Game of Life, using BA modeling. 9. Artificial life. Basic definitions and existing systems and models. Tierra, biomorf, Sbeat, Sbart, Eden, Galapagos ... Self-reproducing automata according to Turing and von Neumann. Langton's loop, computer viruses and artificial life. Artificial Life and edge chaos (according to Kaufmann) 10. Neural Networks (ANN). History and basic principles of NS. The training set and its use NS. The basic types of networks and their applications to different types of problems. 11. 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. 12. Immunological systems (IS). The principle of the IS, the IS limits, algorithms implementing IS imunotronics. 13. Swarm Intelligence (SI). Basic concepts and definitions, representative algorithms SI - Particle Swarm, scatter search, ant colony optimization, swarm robotic, artificial evolution complex systems. 14. DNA computing. DNA computing as part of bioinformatics, DNA and binary representation according to Adlemann. Watson Crickův machine. Mathematical modeling operations on DNA. Laboratories (for PC classrooms): The seminar will focus on the practical application of the discussed techniques and solutions of selected problem examples. - Creation of a single basic framework for bio-inspired algorithms on the principles of GUI, 1 week - Creation of a module for generating population and fitness function, 1 week - Creation of a module selection techniques for parents (suitable candidates) to create offspring (better solution), 1 week - Creation of a module for crossover, 1 week - Creation of a modules of evolutionary algorithms, 4 weeks - Creation of a modules of symbolic regression, 4 weeks - Creation of a module with a simple cellular automaton, 1 week | ||||||

Recommended or Required Reading | ||||||

Required Reading: | ||||||

Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London
Davis L. 1996, Handbook of Genetic Algorithms, International Thomson Computer Press, ISBN 1850328250 Koza J.R. 1998, Genetic Programming, MIT Press, ISBN 0-262-11189-6 Price,K.,Storn,R.,etal.:DifferentialEvolution-APracticalApproachtoGlobalOptimization. Springer, Heidelberg | ||||||

1. Zelinka I., Oplatková Z., Šeda M., Ošmera P., Včelař F., Evoluční výpočetní
techniky, principy a aplikace, BEN, 2008, Praha 2. Kvasnička V., Pospíchal J., Tiňo P., Evolučné algoritmy, STU Bralislava, ISBN 80-227-1377-5, 2000 3. Zelinka I., Včelař F., Čandík M., Fraktální geometrie – principy a aplikace, BEN, 2006, 160 p., ISBN 80-7300-191-8 4. Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London 5. Davis L. 1996, Handbook of Genetic Algorithms, International Thomson Computer Press, ISBN 1850328250 6. Koza J.R. 1998, Genetic Programming, MIT Press, ISBN 0-262-11189-6 7. Price,K.,Storn,R.,etal.:DifferentialEvolution-APracticalApproachtoGlobalOptimization. Springer, Heidelberg | ||||||

Recommended Reading: | ||||||

Ilachinsky A., Cellular Automata: A Discrete Universe, World Scientific Publishing,
ISBN 978-9812381835, 2001 Hilborn R.C.1994, Chaos and Nonlinear Dynamics, Oxford University Press, ISBN 0-19-508816-8, 1994 Gheorghe Paun (Author), Grzegorz Rozenberg (Author), Arto Salomaa, DNA Computing: New Computing Paradigms, Springer, ISBN 978-3540641964 | ||||||

1. Mařík V. Štěpánková O., Lažanský J., Umělá inteligence IV, Academia, Praha,
ISBN 80-200-1044-0, 2004 2. Mařík V. Štěpánková O., Lažanský J., Umělá inteligence III, Academia, Praha, ISBN 80-200-0472-6, 2001 3. Ilachinsky A., Cellular Automata: A Discrete Universe, World Scientific Publishing, ISBN 978-9812381835, 2001 Hilborn R.C.1994, Chaos and Nonlinear Dynamics, Oxford University Press, ISBN 0-19-508816-8, 1994 Gheorghe Paun (Author), Grzegorz Rozenberg (Author), Arto Salomaa, DNA Computing: New Computing Paradigms, Springer, ISBN 978-3540641964 | ||||||

Planned learning activities and teaching methods | ||||||

Lectures, Tutorials | ||||||

Assesment methods and criteria | ||||||

Tasks are not Defined |