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Lecturer(s)
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Šenkeřík Roman, prof. Ing. Ph.D.
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Viktorin Adam, Ing. Ph.D.
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Course content
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- Introduction to evolutionary computing (EVT), history and current trends. Parallel with processes in biology and basic concepts, EVT classification, No Free Lunch theorem. - Benchmarking of algorithms, Population creation, Individual constraints and objective function constraints such as soft constraints and hard constraints. The penalization of the objective function. Different types of individual coding, work with integer and discrete values. - Evolutionary strategies: single and multi-member, CMAES. - Genetic algorithms. - Differential evolution. - Self-organizing migration algorithm. - Swarm algorithms - Swarm intelligence: Introduction, PSO algorithm. - Other modern swarm algorithms (ABC, Ant Colony Optimization, Firefly algorithm, and others). - Other types of evolutionary optimization techniques - hybrid strategies, scatter search, artificial immune system, and others. - Evolutionary symbolic regression - basic concepts and principles. Genetic programming - Analytical programming and Grammatical evolution. - Multi-criteria, many criteria, and dynamic optimization problems and their solutions using EVT. - Use of evolutionary computational techniques in interdisciplinary real applications.
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Learning activities and teaching methods
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Lecturing, Exercises on PC
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| prerequisite |
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| Knowledge |
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| Knowledge from areas: Mathematical Informatics Programming Artificial intelligence |
| Knowledge from areas: Mathematical Informatics Programming Artificial intelligence |
| learning outcomes |
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| 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 |
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| 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 |
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| Knowledge |
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| Lecturing |
| Lecturing |
| Exercises on PC |
| Exercises on PC |
| assessment methods |
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| Oral examination |
| Oral examination |
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Recommended literature
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Kacprzyk J, Pedrycz, W. Springer handbook of computational intelligence. 2015.
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Koza, J. R. Genetic Programming. Cambridge : MIT Press, 1998. ISBN 0-262-11189-6.
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Koza, John R. Genetic Programming : Darwinian Invention and Problem Solving. San Francisco : Morgan Kaufmann Publishers, 1999. ISBN 1558605436.
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Mařík V. Štěpánková O., Lažanský J. Umělá inteligence IV. Academia, Praha, 2004. ISBN 80-200-1044-0.
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O'Neill, Michael. Grammatical evolution : evolutionary automatic programming in an arbitrary language. Boston : Kluwer Academic Publishers, 2003. ISBN 1402074441.
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Posíchal, Jiří. Evolučné algoritmy. 1. vyd. Bratislava : STU, 2000. ISBN 8022713775.
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Simon, D. Evolutionary optimization algorithms: biologically-inspired and population-based approaches to computer intelligence.. 2013. ISBN 978-0-470-93741-9.
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Yang X.S. Recent advances in swarm intelligence and evolutionary computation. 2015. ISBN 978-3-319-13825-1.
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Zelinka I., Oplatková Z., Šeda M., Ošmera P., Včelař F. Evoluční výpočetní techniky, principy a aplikace. 2009. ISBN 978-80-7300-218-3.
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Zelinka I., Snášel V., Abraham A. Handbook of optimization: from classical to modern approach. Berlin, 2013. ISBN 978-3-642-30503-0.
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