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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 Algorithms and Classification
Hurta, Martin ; Sekanina, Lukáš (referee) ; Drahošová, Michaela (advisor)
The aim of this work is to automatically design a program that is able to detect dyskinetic movement features in the measured patient's movement data. The program will be developed using Cartesian genetic programming equipped with coevolution of fitness predictors. This type of coevolution allows to speed up a design performed by Cartesian genetic programming by evaluating a quality of candidate solutions using only a part of training data. Evolved classifier achieves a performance (in terms of AUC) that is comparable with the existing solution while achieving threefold acceleration of the learning process compared to the variant without the fitness predictors, in average. Experiments with crossover methods for fitness predictors haven't shown a significant difference between investigated methods. However, interesting results were obtained while investigating integer data types that are more suitable for implementation in hardware. Using an unsigned eight-bit data type (uint8_t) we've achieved not only comparable classification performance (for significant dyskinesia AUC = 0.93 the same as for the existing solutions), with improved AUC for walking patient's data (AUC = 0.80, while existing solutions AUC = 0.73), but also nine times speedup of the design process compared to the approach without fitness predictors employing the float data type, in average.
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Support for Codenames Game on Mobile Phone with OS Android
Hurta, Martin ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an support application for word association board game Codenames on mobile phones with operating system Android. The solution consists of detection and recognition of the game board using the OpenCV and Tess-two libraries and Google Firebase ML Kit tools and providing support during the game, including an optional level of assistance and the ability to play on multiple devices with Google Play Games services. These features motivate the user to further use the application and provide data in~the form of generated game records, that are useful for further development and validation of association models or strategies for automatic playing.
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Data Analysis of Heatmap of Brno
Kozubek, Jakub ; Hurta, Martin (referee) ; Mrázek, Vojtěch (advisor)
The thesis deals with the analysis of temperature data for the area of the city of Brno. It is focused on the design and verification of hypotheses for warming and cooling parts of the city. The result of the work are methods for statistical testing and for regression analysis implemented in the Python programming language, the results obtained using these methods and their subsequent interpretation in relation to the originally proposed hypotheses.
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Evolutionary Approach to the Traveling Thief Problem
Fodor, Dávid ; Hurta, Martin (referee) ; Sekanina, Lukáš (advisor)
This thesis presents design of an evolutionary algorithm for solving the Traveling thief problem (TTP), which is composed of two interconnected subproblems, the traveling salesperson problem (TSP) and the knapsack problem (KP). The proposed algorithm contains multiple variations of evolutionary algorithm. It is based on the genetic algorithm, the evolutionary algorithm (1+1), and their combination. The algorithm is implemented and tested on official TTP benchmark instances. The best variation of the proposed evolutionary algorithm is chosen and compared with random search and the best publicly available solutions for tested problem instances.
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Evolutionary Optimization of the EEG Classifier Feature Extractor
Ovesná, Anna ; Hurta, Martin (referee) ; Mrázek, Vojtěch (advisor)
This work focuses on the optimisation of EEG signal classification of alcoholics and control subjects using evolutionary algorithms with a multi-objective approach. The main goal is to maximise the accuracy, sensitivity and specificity of the classification algorithm and minimise the number of features used. Four different classifiers are used, namely Support Vector Machine, k-nearest neighbors, Naive Bayes and AdaBoost. The selection of the best features is optimised using three different evolutionary approaches, two of which convert multi-objective optimisation to single-objective using weighted summation or restricting the maximum number of features. The Pareto optimal solutions are found by the NSGA-II algorithm. Results show that the evolutionary algorithms, combined with appropriate classifiers, reliably distinguish a person with a tendency to alcoholism from one with a healthy relationship towards alcohol.
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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 Algorithms and Classification
Hurta, Martin ; Sekanina, Lukáš (referee) ; Drahošová, Michaela (advisor)
The aim of this work is to automatically design a program that is able to detect dyskinetic movement features in the measured patient's movement data. The program will be developed using Cartesian genetic programming equipped with coevolution of fitness predictors. This type of coevolution allows to speed up a design performed by Cartesian genetic programming by evaluating a quality of candidate solutions using only a part of training data. Evolved classifier achieves a performance (in terms of AUC) that is comparable with the existing solution while achieving threefold acceleration of the learning process compared to the variant without the fitness predictors, in average. Experiments with crossover methods for fitness predictors haven't shown a significant difference between investigated methods. However, interesting results were obtained while investigating integer data types that are more suitable for implementation in hardware. Using an unsigned eight-bit data type (uint8_t) we've achieved not only comparable classification performance (for significant dyskinesia AUC = 0.93 the same as for the existing solutions), with improved AUC for walking patient's data (AUC = 0.80, while existing solutions AUC = 0.73), but also nine times speedup of the design process compared to the approach without fitness predictors employing the float data type, in average.
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Support for Codenames Game on Mobile Phone with OS Android
Hurta, Martin ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an support application for word association board game Codenames on mobile phones with operating system Android. The solution consists of detection and recognition of the game board using the OpenCV and Tess-two libraries and Google Firebase ML Kit tools and providing support during the game, including an optional level of assistance and the ability to play on multiple devices with Google Play Games services. These features motivate the user to further use the application and provide data in~the form of generated game records, that are useful for further development and validation of association models or strategies for automatic playing.
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