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Generation of images and animations using L-systems
Jaška, Milan ; Holan, Tomáš (referee) ; Mráz, František (advisor)
Main goal of this thesis is to design system for editing and visualizing planar and cubical scenes consisting of elementary objects and objects described by Lindenmayers systems. Planar scenes visualization will be straight and cubical scenes visualization will be done through VRML language. VRML data will be visualized using external program. System should be able to capture chosen stages of Lindenmayer objects development as pictures so that it is possible to animate the development of object.
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Searching for similar secondary structures in RNA
Vojtek, Daniel ; Hoffmann, Petr (referee) ; Mráz, František (advisor)
In contrast to DNA, the RNA strings fold into complicated secondary structures, which are highly important for their function. Main goal of this work was to create an application, which allows designing a detailed pattern of RNA secondary structure and then finding similar ones in the given set of RNA. The application has also direct access to GenBank database, from which it can download RNA sequences according to the user-defined query. Sequences without secondary structure are folded using a function from Vienna RNA Package. A part of the application is a graphic user interface, which provides comfortable way to design a pattern and allows the browsing found similar secondary structures. The work also presents basic principles of searching of similar RNA secondary structures and its usage in building phylogenetic trees. Description of all used algorithms is present too.
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Machine learning of formal languages
Klonfar, Matěj ; Mráz, František (advisor) ; Plátek, Martin (referee)
In the present work I study the task of machine learning of formal languages. The task of the work is to design and implement the program with anyone will be able to study the progress and results of algorithms for learning languages. With this program is possible to run algorithms for learning from examples. Examples could be sets of positive and negative examples of the result languages or sets expressed by teacher who knows the result language. The main task is to design easily extensible application with a view to inserting add-ins without no limits of used representation of learned languages or language complexity.
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Attractors of cellular automata
Zahradník, Ondřej ; Mráz, František (referee) ; Kůrka, Petr (advisor)
Two heuristic algorithms Omega and Spread are described in this thesis. Algorithm Omega searches for maximal attractors of given cellular automaton. Attractors are constructed as forward images of the join of simple invariant subshifts of finite type, which are contained in the maximal attractor. This join is still contained in the maximal attractor. Afterwards the algorithm searches for a invariant image of this join. The maximal attractor was found if the invariant image was found and if such image has special property of decreasing preimages. The construction of maximal attractor was generalized to shift-invariant attractors by algorithm Spread, which searches for spreading sets of given cellular automaton. There are three undecidable questions. The search for the invariant image, test if such invariant image has the property of decreasing preimages and the search for spreading sets of given cellular automaton. Both algorithms has been tested on the class of elementary cellular automata.
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Improving and extending the multiple sequence alignment suite PRALINE
Hudeček, Jan ; Petříčková, Zuzana (referee) ; Mráz, František (advisor)
The aim of this work is to study potential improvements in the core routines of multiple sequence alignment suite PRALINE. A general overview of multiple sequence alignment methods used with emphasis on representation of the alignment core is given. A new option for aligning sequence profiles was implemented and its usefulness assessed. This option allows a user to input a profile which is used in an advanced phase of the progressive protocol as if it was a result of the previous steps. Two new protocols using profile Hidden Markov models (HMM) and their alignment were implemented and tested. The HMMGUIDE protocol creates for each sequence a preprofile consisting of segments of other sequences with high local similarity. HMM is generated from each preprofile by HMMER, and alignment of every pair is scored by PRC. The protocol then progressively aligns the sequence whose HMMs achieved the best score. The PRCALIGN protocol works similarly but aligns the sequences according to the best alignment of the HMMs. While not all test alignments were finished successfully for both protocols, the results constitute a statistically significant improvement over the original PRALINE protocol.
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Learning Restricted Restarting Automata using Genetic Algorithm
Basovník, Stanislav ; Plátek, Martin (referee) ; Mráz, František (advisor)
Restarting automata are linguistically motivated models for language representation. The main goal of this work is to propose a suitable version of restarting automaton for learning from positive and negative samples using genetic algorithms. We also characterize the class of languages accepted by limited context restarting automata with respect to the Chomsky hierarchy. The proposed learning algorithm is compared to two well-known methods for learning languages from positive and negative samples - RPNI and LARS. A tool for learning the restricted version of restarting automaton is developed as a part of this work. Examples of usage and user guide are included in this work.
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Self-organization and morphing
Lessner, Daniel ; Mráz, František (referee) ; Mrázová, Iveta (advisor)
Morphing is a well-known visual effect. It is based on a uent transition of one image into another one, metamorphosis of a movie character into a bear is a possible example. Realization of such an effect requires accurate, concentrated and expensive effort of an animator. Development of tools and methods for problem solving comparable with human intelect is the subject of arti tial intelligence. Systems working with no human adjustments are often based on self-organisation. Self-organisation of a system is the appearance of complex behavior that isolated parts of the system couldn't reach. This thesis examines possibilities of application of self-organisational methods of artifi tial intelligence in morphing with the goal of reduction of the human assistence. The thesis includes information about some drafted techniques and results of experiments with the most successful technique. Experiments imply that it is possible to reach good results without human assistance if certain conditions are met.
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Evolution of algorithm for artificial creatures
Kohout, Jan ; Mráz, František (referee) ; Holan, Tomáš (advisor)
The task of this work is to create an application which allows performing of experiments with evolution of artificial creatures. Behavior of each creature is managed by its algorithm. The algorithm is able to use creature's memory cells (which represent logical variables) and is the genome of the creature. By the crossing-over and mutation, the algorithm evolves. The second aim of this work is to make some tests with the application and describe their results.
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A database for RNA secondary structures
Tattermusch, Jan ; Mráz, František (advisor) ; Hoffmann, Petr (referee)
In the presented work we study RNA primary and secondary structures and we select suitable models for their computational representation. Next, we state criteria for primary and secondary structure comparison. Based on this criteria, we select tree alignment as a suitable method for implementation of structural search. We also mention some speci cs of structural search task and propose some possible modi cations that can speed-up the search. An important part of this work is web application "RNA Secondary Structure Database", which implements structural and sequence-structural search.
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Grammatical evolution
Nohejl, Adam ; Iša, Jiří (referee) ; Mráz, František (advisor)
Grammatical evolution (GE) is a recent grammar-based approach to genetic programming that allows development of solutions in an arbitrary programming language. Its existing implementations lack documentation and do not provide reproducible results suitable for further analysis. This thesis summarises the methods of GE and the standard methods used in evolutionary algorithms, and reviews the existing implementations, foremost the only actively developed one, GEVA. A new comprehensive software framework for GE is designed and implemented based on this review. It is modular, well-documented, portable, and gives reproducible results. It has been tested in two benchmark applications, in which it showed competitive results and outperformed GEVA 10 to 29 times in computational time. It is also shown how to further improve the performance and results by using techniques unsupported by GEVA, including new modications to the previously published methods of bit-level mutation and "sensible" initialisation. The thesis and the software together form a solid foundation for further experiments and research.
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