Kartézské genetické programován (CGP) je evoluc inspirovaná metoda strojového učen, která je primárně určená pro automatizovaný návrh programů a čslicových obvodů. CGP je úspěšné v řešen mnoha úloh z reálného světa. Avšak k nalezen inovativnch řešen obvykle potřebuje značný výpočetn výkon. Každý kandidátn program navržený pomoc CGP mus být spuštěn, aby se zjistilo, do jaké mry tento program řeš zadaný problém, a mohla mu být přiřazena fitness hodnota. Právě vyhodnocen fitness bývá výpočetně nejnáročnějš část návrhu pomoc CGP. Tato práce se zabývá využitm koevoluce prediktorů fitness v CGP za účelem zrychlen procesu evolučnho návrhu prováděného pomoc CGP. Prediktor fitness je malá podmnožina trénovacch dat použvaná pro rychlý odhad fitness hodnoty namsto náročného vyhodnocen objektivn fitness hodnoty. Koevoluce prediktorů fitness je optimalizačn metoda modelován fitness, která snižuje náročnost a frekvenci výpočtu fitness. V této práci je koevolučn algoritmus přizpůsoben pro CGP a jsou představeny a zkoumány tři přstupy k zakódován prediktorů fitness. Představená metoda je experimentálně vyhodnocena v pěti úlohách symbolické regrese a v úloze návrhu obrazových filtrů. Výsledky experimentů ukazuj, že pomoc této metody lze významně snžit výpočetn čas, který CGP potřebuje pro řešen zkoumané třdy úloh.

This thesis deals with image filter design using coevolutionary algorithms. It contains a description of evolutionary algorithms, focusing on genetic programming, cartesian genetic programming and coevolution, the reader can learn about image filters too. The next chapters contain the design of image filters and noise detectors using cooperative coevolution, and the implementation and testing of the proposed filter. In the last chapter the proposed filter is compared to other filters created using evolutionary algorithms but without coevolution.

The aim of this work is to design and implement a program for automated design of convolutional neural networks (CNN) with the use of evolutionary computing techniques. From a practical point of view, this approach reduces the requirements for the human factor in the design of CNN architectures, and thus eliminates the tedious and laborious process of manual design. This work utilizes a special form of genetic programming, called Cartesian genetic programming, which uses a graph representation for candidate solution encoding.This technique enables the user to parameterize the CNN search process and focus on architectures, that are interesting from the view of used computational units, accuracy or number of parameters. The proposed approach was tested on the standardized CIFAR-10dataset, which is often used by researchers to compare the performance of their CNNs. The performed experiments showed, that this approach has both research and practical potential and the implemented program opens up new possibilities in automated CNN design.

Real-time strategy games are an exciting area of research, as creating a game AI poses many challenges - from managing a single unit to completing an objective of the game. This thesis explores possible solutions to this task, using genetic programming and neuroevolution. It presents and compares findings and differences between the models. Both methods performed reasonably well, but genetic programming was found to be a bit more effective in performance and results.

Tato diplomová práce pojednává o dvou algoritmech pro dolování z proudu dat - Very Fast Decision Tree (VFDT) a Concept-adapting Very Fast Decision Tree (CVFDT). Je vysvětlen princip klasifikace rozhodovacím stromem. Je popsána základní myšlenka konstrukce stromu Hoeffding Tree, který je základem pro algoritmy VFDT a CVFDT. Tyto algoritmy jsou poté rozebrány detailněji. Dále se tato práce zabývá návrhem algoritmu Genetického Programování (GP), který je použit pro vytváření klasifikátoru obrazových dat. Vytvořený klasifikátor je použit jako alternativní způsob klasifikace objektů v obraze ve frameworku Viola-Jones. V práci je rozebrána implementace algoritmů, které jsou implementovány v jazyce Java. Algoritmus GP je integrován do knihovny “Image Processing Extension” programu RapidMiner. Algoritmy VFDT a CVFDT jsou testovány na syntetických a reálných textových datech. Algoritmus GP je testován na klasifikaci obrazových dat a následně vytvořený klasifikátor je otestován na detekci obličejů v obraze.

This master thesis describes stochastic optimization algorithms inspired in nature that use population of individuals - evolutionary algorithms. Genetic programming and its variant - cartesian genetic programming is described in a greater detail. This thesis is further focused on the analysis and visualization of digital circuit evolution records. Existing tools for visualization of the circuit evolution were analysed, but because no suitable tool allowing complex analysis of the circuit evolution was found, a new set of functions was proposed and the principles of a new tool were formulated. These functions were implemented in form of an interactive GUI application in Java programming language. The application was described in detail and then used for analysis of digital circuit evolution records.

This thesis deals with the usage of coevolution in the task of symbolic regression. Symbolic regression is used for obtaining mathematical formula which approximates the measured data. It can be executed by genetic programming - a method from the category of evolutionary algorithms that is inspired by natural evolutionary processes. Coevolution works with multiple evolutionary processes that are running simultaneously and influencing each other. This work deals with the design and implementation of the application which performs symbolic regression using coevolution on test-based problems. The test set was generated by a new method, which allows to adjust its size dynamically. Functionality of the application was verified on a set of five test tasks. The results were compared with a coevolution algorithm with a fixed-sized test set. In three cases the new method needed lesser number of generations to find a solution of a desired quality, however, in most cases more data-point evaluations were required.

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.

This thesis deals with the polymorphic image filter design. The study includes polymorphic circuits, their theoretical base and practical applications. It further focuses on the cartesian genetic programming that can be used for an evolutionary design of some types of image filters. The thesis continues with the specification of the evolutionary algorithm to be used for the design of the polymorphic image filters. The implementation of the algorithm is described in two versions -- a standard one running only on a CPU and an accelerated one that partially uses the GPU. Several polymorphic image filters are designed by means of the algorithm.

The aim of this diploma thesis is to survey the area of multiobjective genetic algorithms and cartesian genetic programming. In detail the NSGAII algorithm and integration of multiobjective optimalization into cartesian genetic programming are described. The method of multiobjective CGP was tested on selected problems from the area of digital circuit design.