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Symbolic Regression and Coevolution
Drahošová, Michaela ; Žaloudek, Luděk (referee) ; Sekanina, Lukáš (advisor)
Symbolic regression is the problem of identifying the mathematic description of a hidden system from experimental data. Symbolic regression is closely related to general machine learning. This work deals with symbolic regression and its solution based on the principle of genetic programming and coevolution. Genetic programming is the evolution based machine learning method, which automaticaly generates whole programs in the given programming language. Coevolution of fitness predictors is the optimalization method of the fitness modelling that reduces the fitness evaluation cost and frequency, while maintainig evolutionary progress. This work deals with concept and implementation of the solution of symbolic regression using coevolution of fitness predictors, and its comparison to a solution without coevolution. Experiments were performed using cartesian genetic programming.
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Comparison of Genetic Programming Variants in the Symbolic Regression Task
Doležal, Petr ; Hurta, Martin (referee) ; Drahošová, Michaela (advisor)
This thesis deals with comparison of genetic programming variants it the task of symbolic regression. Time to converge and quality of evolved solutions are evaluated on nine chosen benchmarks. In particular, tree-based genetic programming, cartesian genetic programming and their modifications using coevolutionary algorithm are investigated. An own implementation of employed methods (without a specific library use) allows to share as much code as possible. Moreover, an analysis of implemented methods efficiency on real world data is provided. Experimental results show that all of the investigated approaches are capable of finding solutions using symbolic regression. Cartesian genetic programming enhanced with coevolution seems to be the most suitable of the investigated approaches in terms of evolved solution quality and time to converge.
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Coevolutionary Algorithm in FPGA
Hrbáček, Radek ; Vašíček, Zdeněk (referee) ; Drahošová, Michaela (advisor)
This thesis deals with the design of a hardware acceleration unit for digital image filter design using coevolutionary algorithms. The first part introduces reconfigurable logic device technology that the acceleration unit is based on. The theoretical part also briefly characterizes evolutionary and coevolutionary algorithms, their principles and applications. Traditional image filter designs are compared with the biologically inspired design methods. The hardware unit presented in this thesis exploits dual MicroBlaze system extended by custom peripherals to accelerate cartesian genetic programming. The coevolutionary image filter design is accelerated up to 58 times. The hardware platform functionality in the task of impulse noise filter design and edge detector design has been empirically analyzed.
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Coevolution in Evolutionary Circuit Design
Veřmiřovský, Jakub ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This thesis deals with evolutionary design of the digital circuits performed by a cartesian genetic programing and optimization by a coevolution. Algorithm coevolves fitness predictors that are optimized for a population of candidate digital circuits. The thesis presents theoretical basis, especially genetic programming, coevolution in genetic programming, design of the digital circuits, and deals with possibilities of the utilization of the coevolution in the combinational circuit design. On the basis of this proposal, the application designing and optimizing logical circuits is implemented. Application functionality is verified in the five test tasks. The comparison between Cartesian genetic programming with and without coevolution is considered. Then logical circuits evolved using cartesian genetic programming with and without coevolution is compared with conventional design methods. Evolution using coevolution has reduced the number of evaluation of circuits during evolution in comparison with standard cartesian genetic programming without coevolution and in some cases is found solution with better parameters (i.e. less logical gates or less delay).
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Detection of Correlated Mutations
Ižák, Tomáš ; Bendl, Jaroslav (referee) ; Martínek, Tomáš (advisor)
Tato práce zkoumá existující možnosti a metody detekce korelovaných mutací v proteinech. Práce začíná teoretickým úvodem do zkoumané problematiky. Využití informací o korelovaných mutacích je především při predikci terciální struktury proteinu či hledání oblastí s významnou funkcí. Dále následuje přehled v současnosti používaných metod detekce a jejich výhody a nevýhody. V této práci jsou zkoumány zejména metody založené na statistice (například Pearsonově korelačním koeficientu nebo Pearsonově chi^2 testu), informační teorii (Mutual information - MI) a pravděpodobnosti (ELSC nebo Spidermonkey). Dále jsou popsány nejdůležitější nástroje s informací o tom, které metody používají a jakým způsobem. Také je diskutována možnost návrhu optimálního algoritmu. Jako optimální z hlediska úspěšnosti detekce je doporučeno využít více zmíněných metod. Také je doporučeno při detekci využít fyzikálně-chemických vlastností aminokyselin. V praktické části byla vyvinuta metoda využívající fyzikálně-chemických vlastností aminokyselin a fylogenetických stromů. Výsledky detekce byly porovnány s nástroji CAPS, CRASP a CMAT.
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Coevolution of Cartesian Genetic Algorithms and Neural Networks
Kolář, Adam ; Král, Jiří (referee) ; Zbořil, František (advisor)
The aim of the thesis is to verify synergy of genetic programming and neural networks. Solution is provided by set of experiments with implemented library built upon benchmark tasks. I've done experiments with directly and also indirectly encoded neural netwrok. I focused on finding robust solutions and the best calculation of configurations, overfitting detection and advanced stimulations of solution with fitness function. Generally better solutions were found using lower values of parameters n_c and n_r. These solutions tended less to be overfitted. I was able to evolve neurocontroller eliminating oscilations in pole balancing problem. In cancer detection problem, precision of provided solution was over 98%, which overcame compared techniques. I succeeded also in designing of maze model, where agent was able to perform multistep tasks.
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Image Classification Using Genetic Programming
Jašíčková, Karolína ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
This thesis deals with image classification based on genetic programming and coevolution. Genetic programming algorithms make generating executable structures possible, which allows us to design solutions in form of programs. Using coevolution with the fitness prediction lowers the amount of time consumed by fitness evaluation and, therefore, also the execution time. The thesis describes a theoretical background of evolutionary algorithms and, in particular, cartesian genetic programming. We also describe coevolutionary algorithms properties and especially the proposed method for the image classifier evolution using coevolution of fitness predictors, where the objective is to find a good compromise between the classification accuracy, design time and classifier complexity. A part of the thesis is implementation of the proposed method, conducting the experiments and comparison of obtained results with other methods.
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Allelopathy in constitution of plant communities
Kučera, Pavel ; Weiser, Martin (advisor) ; Hadincová, Věroslava (referee)
Allelopathy, the ability of some plants to decrease the fitness of other plants by secondary metabolites, has been known for a very long time. The scientific community paid great attention to its research. Despite this, there is still a lot of ambiguity surrounding this phenomenon. In the past, the role of allelopathy in the constitution of plant communities had been often overlooked. The situation improved slightly in the past years. Several new articles summarizing information about the ecological aspect of allelopathy have been published. However, there are many unanswered questions about its widespread in plant communities and the degree of its influence on the ecosystem. This bachelor thesis is focused on summarizing contemporary knowledge about the influence of allelopathy, mainly from the ecological viewpoint, and presenting possible directions for future research. There is included a short list of the most common allelochemicals, basic principles of the functioning of allelopathy, its widespread through plant taxa, and its effect on the plant communities, mutualistic microorganisms, and the whole ecosystem. There is also mentioned the role of allelopathy in the process of invasion of alien species and at the end of the thesis, I discussed possible utilization of accumulated information in...
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Plant adaptations for pollination by nocturnal animals
Bakovská, Julie ; Tropek, Robert (advisor) ; Sklenář, Petr (referee)
Nocturnal pollination is a part of the reproductive process of plants. The night is characterized by changes in abiotic factors, mainly by a decrease in solar radiation and temperature, while plants and pollinators adapt to these conditions. Plants and pollinators adapt to each other throught the process of coevolution. As a result plants present traits preferred by their pollinators. Sets of convergent traits shared by plants pollinated by single functional group are called pollination syndromes and include reward type, scent, colour and flower morphology. The pollination syndromes and other adaptations of plants pollinated by nocturnal pollinators are presented in this thesis, in the context of adaptations of pollinators' senses to nighttime conditions. Simultaneously, it is referred to the evolutionary reasons for the transition of activity to the night. Significant nocturnal pollinators, associated with pollination syndromes include moths, divided into hawkmoths (sphingophily pollination syndrome) and other nocturnal moths (phalaenophily), bats (chiropterophily) and non-flying mammals (therophily). Other important nocturnal pollinators exhibiting adaptations to nocturnal pollination are beetles and nocturnal bees.
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