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Prediction of Protein Stability upon Mutations Using Evolution Strategy
Pavlík, David ; Martínek, Tomáš (referee) ; Bendl, Jaroslav (advisor)
This master's thesis deals with the matter of predicting the effects of aminoacid substitutions on protein stability. The main aim is to design meta-classifier that combines the results of the selected prediction tools. An evolution strategy was used to find the best weights for each of the selected tools with the aim of achieving better prediction performance compared to that achieved by using these tools separately. Five different and obtainable prediction tools were selected and their prediction outputs were weighted. Two different approaches of evolution strategy are investigated and compared: evolution strategy with the 1/5-rule and evolution strategy with the type 2 of control parameters self-adaptation. Two independent datasets of mutations were created for training and evaluating the performance of designed meta-classifier. The performed experiments and obtained results suggest that the evolution strategy could be considered as a~beneficial approach for prediction of protein stability changes. However, the special attention must be paid to careful selection of tools for integration and compilation of training and testing datasets.
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Optimization of Aircraft Tracker Parameters
Samek, Michal ; Vlk, Jan (referee) ; Smrž, Pavel (advisor)
Diplomová práce se zabývá optimalizací systému pro sledování letadel, využívaného pro řízení letového provozu. Je popsána metodika vyhodnocování přesnosti sledovacího systému a přehled relevantních algoritmů pro sledování objektů. Dále jsou navrženy tři přístupy k řešení problému. První se pokouší identifikovat parametry filtrovacích algoritmů pomocí algoritmu Expectation-Maximisation, implementací metody maximální věrohodnosti. Druhý přístup je založen na prostých odhadech parametrů normálního rozložení z naměřených a referenčních dat. Nakonec je zkoumána možnost řešení pomocí optimalizačního algoritmu Evoluční strategie. Závěrečné vyhodnocení ukazuje, že třetí přístup je pro daný problém nejvhodnější.
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Evolutionary Design of Quantum Operator
Kraus, Pavel ; Mrázek, Vojtěch (referee) ; Bidlo, Michal (advisor)
The goal of this thesis is to utilize various evolutionary algorithms for quantum operator design in the form of unitary matrices in direct representation. Evolution strategy, differential evolution, Particle Swarm Optimization and artificial bee colony algorithms were chosen. In this thesis, the third and fourth algorithms were used for the first time in relation to quantum operator design. The experiments have shown that the utilization of direct representation gives results of acceptable quality.
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Prediction of Protein Stability upon Amino Acid Mutations Using Evolution Strategy
Kadlec, Miroslav ; Burgetová, Ivana (referee) ; Bendl, Jaroslav (advisor)
This thesis is focused on predicting the impact of amino acid substitution on protein stability. The main goal is to create a consensual predictor that uses the outputs of chosen existing tools in order to improve accuracy of prediction. The optimal consensus of theese tools was designed using evolution strategies in three variants: 1/5 success rule, self-adaptation variant and the CMA-ES method. Then, the quality of calculated weight vectors was tested on the independent dataset. Although the highest prediction performance was attained by self-adaptation method, the differences between all three variants were not significant. Compared to the individual tools, the predictions provided by consensual methods were generally more accurate - the self-adaptation variant imporved the Pearson's corelation coeficient of the predictions by 0,057 on the training dataset. On the testing dataset, the improvement of designed method was smaller (0,040). Relatively low improvement of prediction performance (both on the training and the testing dataset) were caused by the fact, that for some records of testing dataset, some individual tools vere not able to provide their results. When omitting these records, consensual method improved the Pearson's corelations coeficient by 0,118.
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Neuroevolution Principles and Applications
Herec, Jan ; Strnadel, Josef (referee) ; Bidlo, Michal (advisor)
The theoretical part of this work deals with evolutionary algorithms (EA), neural networks (NN) and their synthesis in the form of neuroevolution. From a practical point of view, the aim of the work is to show the application of neuroevolution on two different tasks. The first task is the evolutionary design of the convolutional neural network (CNN) architecture that would be able to classify handwritten digits (from the MNIST dataset) with a high accurancy. The second task is the evolutionary optimization of neurocontroller for a simulated Falcon 9 rocket landing. Both tasks are computationally demanding and therefore have been solved on a supercomputer. As a part of the first task, it was possible to design such architectures which, when properly trained, achieve an accuracy of 99.49%. It turned out that it is possible to automate the design of high-quality architectures with the use of neuroevolution. Within the second task, the neuro-controller weights have been optimized so that, for defined initial conditions, the model of the Falcon booster can successfully land. Neuroevolution succeeded in both tasks.
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Functional Annotation of Nucleotide Polymorphism Using Evolution Strategy
Šalanda, Ondřej ; Martínek, Tomáš (referee) ; Bendl, Jaroslav (advisor)
This thesis brings a new approach to the prediction of the the effect of amino acid substitution. The main goal is to create a new meta-tool, which combines evaluations of eight already implemented prediction tools. The use of weighted consensus over those tools should lead to better accuracy and versatility of prediction. The novelty of developed tool lies in involving evolution strategy with experimentally defined parameters as a way to determine the best weight distribution. At the end, a complex comparison and evaluation of results is given.
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Quantum-Inspired Optimisation Algorithms
Kosík, Dominik ; Sekanina, Lukáš (referee) ; Bidlo, Michal (advisor)
The focus of this work is an implementation of the chosen quantum-inspired optimisation algorithm and its modifications, which will be compared at the end of the work. As the optimisation algorithm was chosen simulated quantum annealing algorithm. The first part of the work will lay the theoretical groundwork of standard optimisation algorithms used in this work, physics from which the inspiration for the simulated quantum annealing originates, and a description of the chosen algorithm. The second part will focus on the implementation of the algorithms on the selected problems. The selected problems are travelling salesman problem, searching rules for cellular automaton and MAX-SAT problem. The last part will contain the proposed modifications of the simulated quantum annealing, a comparison of the basic variant and standard optimisations algorithms, and an evaluation of the results.
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Evolutionary Optimisation of Analogue Circuits
Mihulka, Tomáš ; Zachariášová, Marcela (referee) ; Bidlo, Michal (advisor)
The aim of this work was to create a system for optimisaton of specific analog circuits by evolution using multiple fitness functions . A set of experiments was run, and the results analyzed to evaluate the feasibility of evolutionary optimisation of analog circuits . A requirement for this goal is the study and choice of certain types of analog circuits and evolutionary algorithms . For the scope of this work , amplifiers and oscillators were chosen as target circuits , and genetic algorithms and evolutionary strategies as evolutionary algorithms . The motivation for this work is the ongoing effort to automate the design and optimisation of analog circuits , where evolutionary optimisation is one of the options .
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Optimizations Methods for Freight Transportation
Gabonay, Michal ; Vašíček, Zdeněk (referee) ; Bidlo, Michal (advisor)
The following work concerns the study of the evolutionary algorithm, which optimizes freight transport planning. The demand for freight transport is constantly increasing nowadays and with creating, implementing and using proper route planning we are able to significantly reduce transportation costs. However, it is preferably to implement it in companies with large numbers of served customers and with a sufficiently large fleet of vehicles. The study starts by defining what fright transport planning problem is and by characterizing its existing specifications and variants. My work proceeds to give a background of the possible solutions to the multifaceted aspects of the problem. The specific subproblem I choose to focus on is the Vehicle routing problem with Pickup and Delivery for which I apply the optimization solution. In the main body of my thesis, I will elaborate on the chosen optimization solution which encompasses the genetic algorithm and evolutionary strategy. The aim of the study is to measure the suitability of the algorithms and techniques used, for which reason the final part of my work will deal with the analysis and evaluation of the experiments.
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Evolutionary Prediction of Time Series
Křivánek, Jan ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
This thesis summarizes knowledge in the field of time series theory, method for time series analysis and applications in financial modeling. It also resumes the area of evolutionary algorithms, their classification and applications. The core of this work combines these knowledges in order to build a system utilizing evolutionary algorithms for financial time series forecasting models optimization. Various software engineering techniques were used during the implementation phase (ACI - autonomous continual integration, autonomous quality control etc.) to ensure easy maintainability and extendibility of project by more developers.
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