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

Course Unit Code | 460-4086/01 | |||||
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Number of ECTS Credits Allocated | 4 ECTS credits | |||||

Type of Course Unit * | Compulsory | |||||

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

Year of Study * | First Year | |||||

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

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

Language of Instruction | Czech | |||||

Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit | |||||

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

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

Summary | ||||||

The course will discuss a wider range of evolutionary computing techniques. Both historically classical techniques and modern algorithms will be mentioned. Evolutionary algorithms and swarm intelligence such as simulated annealing, genetic algorithm, differential evolution, particle swarm, SOMA and others will be discussed. In the second part, the student gets acquainted with symbolic regression and its use in the synthesis of algorithms, classifiers or control programs. After completing the course, the student should have comprehensive knowledge of the above areas, including the possibility of their use. Part of the course is laboratory exercises, in which students will practice both programmings of selected algorithms and their application to solving practical problems. | ||||||

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

The aim of the course is to acquaint its students with modern computational methods derived from evolutionary and biological processes (evolutionary algorithms, cellular automata, etc.). The graduate will learn to program and use popular algorithms in the field of evolution and swarm intelligence and apply them to real problems. He will also gain an overview of modern computational procedures based on principles observed from biological processes and dynamics. Upon successful completion of the course, the graduate will be able to apply the methods discussed in the course to real problems of practice. | ||||||

Course Contents | ||||||

Lectures:
1.Current state in the field of soft computing, fuzzy logic, neural networks, evolutionary computing (EVT). Classification of evolutionary computing techniques, historical facts, current trends in the field of EVT. The central dogma of EVT according to Darwin and Mendel. 2. No Free Lunch Theorem. Computational complexity and physical limits of algorithms. 3. Limitations placed on the cost function and parameters of the individual. Penalty and its impact on the geometry of the cost function. Work with real, integer and discrete values of individual parameters. 4. Test benchmark function. 5. Multi and many-objective optimization and Pareto set. Critical situations in the algorithm run and their solution. 6. Blind search and climbing algorithm. 7. Genetic algorithms. GA terminology. Principle of operation, Hybrid GA, messy GA, parallel GA, migration and diffusion model. 8. Evolutionary strategies. Two-member ES: (1 + 1) -ES. Multipart ES: (μ + λ) -ES and (μ, λ) -ES. Multipart ES: (μ + λ) -ES and (μ, λ) -ES. Adaptive ES. 9. Particle swarm. Scatter Search. AntColony Optimization. 10. SOMA: Self-organizing Migration Algorithm, the principle of operation and used algorithm strategies: ATO, ATR, ATA and ATAA. 11. Differential evolution, principle 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. 12. Swarm intelligence (SI). Basic terms and definitions, representative SI algorithms - particle swarm, scatter search, ant colony optimization, swarm robotic, artificial evolution of complex systems. 13. Techniques of symbolic regression: genetic programming, grammatical evolution. Alternatives: Analytical Programming, Probabilistic Incremental Program Evolution - PIPE, Gene Expression Programming, Multiexpression Programming and more. 14. Case studies Laboratories (for PC classrooms): The seminar will focus on the practical application of the discussed techniques and solutions of selected problem examples. • Implementation of selected benchmark functions (optimization problems) • Implementation of the Blind Algorithm and the Climbing Algorithm • Implementation of the Genetic Algorithm and its application to the business traveller problem (TSP) • Implementation of Differential Evolution • Implementation of Evolutionary Strategies • Implementation of the Particle Swarm Optimization algorithm using inertia • Implementation of a Self-Organizing Migration Algorithm (SOMA), comparison of the behavior of SOMA and PSO algorithms • Implementation of Firefly algorithm • Implementation of the Ant Colony Optimization (ACO) algorithm and its application to the business traveller problem. Comparison of ACO and GA in terms of convergence speed and accuracy of the resulting solution • Teaching-learning based algorithm • Multiobjective optimization. Implementation of NSGA II algorithm (Non-dominated Sorting Genetic Algorithm) | ||||||

Recommended or Required Reading | ||||||

Required Reading: | ||||||

1. Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London
2. Davis L. 1996, Handbook of Genetic Algorithms, International Thomson Computer Press, ISBN 1850328250 3. Koza J.R. 1998, Genetic Programming, MIT Press, ISBN 0-262-11189-6 4. 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: | ||||||

5. Ilachinsky A., Cellular Automata: A Discrete Universe, World Scientific Publishing, ISBN 978-9812381835, 2001
6. Hilborn R.C.1994, Chaos and Nonlinear Dynamics, Oxford University Press, ISBN 0-19-508816-8, 1994 7. Gheorghe Paun (Author), Grzegorz Rozenberg (Author), Arto Salomaa, DNA Computing: New Computing Paradigms, Springer, ISBN 978-3540641964 | ||||||

8. Mařík V. Štěpánková O., Lažanský J., Umělá inteligence IV, Academia, Praha,
ISBN 80-200-1044-0, 2004 9. Mařík V. Štěpánková O., Lažanský J., Umělá inteligence III, Academia, Praha, ISBN 80-200-0472-6, 2001 10. Ilachinsky A., Cellular Automata: A Discrete Universe, World Scientific Publishing, ISBN 978-9812381835, 2001 11. Hilborn R.C.1994, Chaos and Nonlinear Dynamics, Oxford University Press, ISBN 0-19-508816-8, 1994 12. 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 | ||||||

Task Title | Task Type | Maximum Number of Points (Act. for Subtasks) | Minimum Number of Points for Task Passing | |||

Credit and Examination | Credit and Examination | 100 (100) | 51 | |||

Credit | Credit | 45 | 25 | |||

Examination | Examination | 55 | 6 |