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Lecturer(s)
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Šenkeřík Roman, prof. Ing. Ph.D.
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Komínková Oplatková Zuzana, prof. Ing. Ph.D.
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Course content
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- Overview of bioinspired optimization techniques. - Methods of parameter adaptation and modern algorithmic strategies. - Possibilities of hybridization of bioinspired optimization techniques. - Platforms for multi and many-criteria optimization. - Techniques enabling optimization in the space of high dimensions (large-scale problems). - Assisted optimization methods for computationally demanding real optimization models (surrogate assisted models). - Theoretical aspects of this modern field, such as convergence and runtime analysis. - Modification of algorithms for operations in discrete space suitable for permutation, combinatorial, and similar derived problems.
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Learning activities and teaching methods
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Individual work of students
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| prerequisite |
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| Knowledge |
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| Knowledge from areas: Mathematical Informatics Optimization Programming Artificial intelligence |
| Knowledge from areas: Mathematical Informatics Optimization Programming Artificial intelligence |
| learning outcomes |
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| The student can describe and analyze various bioinspired optimization methods, including evolutionary algorithms and swarm intelligence. |
| The student can describe and analyze various bioinspired optimization methods, including evolutionary algorithms and swarm intelligence. |
| The student understands the principles of adaptation and learning techniques for control parameters of bioinspired algorithms. |
| The student understands the principles of adaptation and learning techniques for control parameters of bioinspired algorithms. |
| The student has knowledge of the possibilities of hybridization and the application of bioinspired optimization techniques for research. |
| The student has knowledge of the possibilities of hybridization and the application of bioinspired optimization techniques for research. |
| The student understands the theoretical aspects of the field, including convergence and runtime analysis, and assisted optimization methods for computationally intensive models. |
| The student understands the theoretical aspects of the field, including convergence and runtime analysis, and assisted optimization methods for computationally intensive models. |
| The student has knowledge of techniques allowing optimization in high-dimensional space and methods suitable for permutation and combinatorial problems. |
| The student has knowledge of techniques allowing optimization in high-dimensional space and methods suitable for permutation and combinatorial problems. |
| Skills |
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| The student can apply bioinspired optimization techniques to solve interdisciplinary optimization problems. |
| The student can apply bioinspired optimization techniques to solve interdisciplinary optimization problems. |
| The student can design and implement hybrid optimization models for specific research tasks. |
| The student can design and implement hybrid optimization models for specific research tasks. |
| The student can optimize the parameters of algorithms to improve their performance and efficiency. |
| The student can optimize the parameters of algorithms to improve their performance and efficiency. |
| The student has skills in testing algorithms using state-of-the-art test suites and platforms. |
| The student has skills in testing algorithms using state-of-the-art test suites and platforms. |
| The student is able to solve high-dimensional and computationally demanding optimization problems using bioinspired methods. |
| The student is able to solve high-dimensional and computationally demanding optimization problems using bioinspired methods. |
| teaching methods |
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| Knowledge |
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| Individual work of students |
| Individual work of students |
| assessment methods |
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| Background research |
| Background research |
| Preparation of a presentation, giving a presentation |
| Preparation of a presentation, giving a presentation |
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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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