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Machine Learning of Representations in Genetic Programming
Pomykal, Šimon ; Piňos, Michal (referee) ; Sekanina, Lukáš (advisor)
The aim of this thesis is to become acquainted with machine learning methods that are used for the automatic design of representations. Specifically, the work focuses on deep learning in the field of genetic programming (GP). Image processing is chosen as a case study, particularly noise reduction methods. By combining the acquired knowledge, a new representation is proposed, intended to replace the syntactic tree in the GP algorithm. This method is obtained using a transformer-type neural network. In conclusion, a modified version of GP that works with the new representation is created. This variant is compared with the original GP using the traditional representation in several experiments.
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Automated Representation Learning for Cartesian Genetic Programming Using Neural Networks
Koči, Martin ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
This master's thesis addresses the integration of neural networks and Cartesian Genetic Programming (CGP). It explores the use of neural networks for automated representation creation for CGP and their application to improve the evolutionary process in CGP. The study covers basic concepts of machine learning, including various types of learning and neural network models. It also touches on evolutionary algorithms with an emphasis on their basic principles, general algorithms, and types of representations. This work also includes principles of representation learning and two fundamental architectures for their creation. It describes the subsequent use of representation learning in genetic programming. The solution design includes data acquisition and preprocessing, representation creation processes, and the utilization of the resulting representations. The thesis also implements two new approaches for creating representations for Cartesian genetic programs. It further explores their use in two new mutation operators, where one is based on direct modification of the vector representation and the other on the selection of genes for mutation based on their similarity. The last of the explored areas is predicting the suitability of candidate solutions using newly emerged representations.
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Evolutionary Circuit Design by Means of Genetic Programming
Synák, Maroš ; Hurta, Martin (referee) ; Bidlo, Michal (advisor)
Tato práce zkoumá aplikaci genetického programování (GP) pro návrh elektronických obvodů, zaměřuje se na sinusové a obdélníkové oscilátory a diskriminátory tónů, s použitím Pythonu a PySpice. Cílem je znovu vytvořit aspekty základní práce Johna Kozy v tomto oboru. Hlavním cílem je posoudit, zda může GP generovat pokročilé elektronické návrhy efektivněji než tradiční metody, přizpůsobujíc přitom Kozy genetické operace - výběr, křížení, mutaci - moderním výzvám v návrhu obvodů. Metodologie zahrnuje vývoj modelů GP pro simulaci evolučního návrhu obvodů, hodnocených prostřednictvím kontrolovaných experimentů. Tyto experimenty testují schopnost modelů vyvíjet se od základních po složité konfigurace, které splňují specifické elektronické funkce. Tato studie nejenže přezkoumává, ale také upravuje Kozy metody, zahrnuje strategie více závislé na počátečním nastavení embrya, aby řídily evoluční proces v generování cílených návrhů. Kromě toho studie zkoumá nedávné metodologie využívané v podobných aplikacích, aby zvýšila adaptabilitu a efektivitu GP. Výsledky ukazují, že zatímco GP může účinně podporovat a zlepšovat návrh elektronických obvodů pro sinusové oscilátory a diskriminátory tónů, jeho aplikace na generování obdélníkových oscilátorů narazí na omezení a vážné problémy. To zdůrazňuje oblasti pro potenciální zlepšení v genetické diverzitě a zdokonalení algoritmů. Celkově tato práce zdůrazňuje potenciál genetického programování v revoluci návrhu elektronických obvodů, což naznačuje další průzkum a zdokonalení Kozy metodologií, které by mohly rozšířit aplikovatelnost GP v oboru. Tato práce představuje jak pokračování, tak evoluci jeho průkopnických úsilí, čímž otevírá cestu pro budoucí inovace v elektronickém inženýrství.
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Genetic Programming with Memory for Symbolic Regression
Jůza, Tadeáš ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
The purpose of this thesis is to evaluate the possibility of extending genetic programming with memory for solving symbolic regression problems. Furthermore, a set of problems for testing the quality of such solutions is developed. The thesis proposes a practical application of such an extension to reduce the energy consumption of loading weights of convolutional neural networks. Instead of retrieving all the weights of the network from external memory, only a small percentage of the weights is retrieved and the remaining ones are generated using the evolved expression. This method was primarily evaluated on reducing the set of weights of convolutional layers of a small convolutional neural network classifying the MNIST dataset. Furthermore, the possibility of generating weights was also tested on other convolutional neural networks solving more complex classification problems. The proposed method has delivered interesting tradeoffs between the classification accuracy and weight memory size.
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Diffusion Evolutionary Algorithm
Mészáros, István ; Pospíchal, Petr (referee) ; Jaroš, Jiří (advisor)
There are new trends in artificial intelligence nowadays. Methods known as evolutionary algorithms are one of them. These algorithms allow us to design and optimize systems using computers. One of the variants of evolutionary algorithms is the diffusion evolutionary algorithm. This type of algorithms is able to run in parallel, and besides that it brings many positive features. The question is under what conditions the diffusion variant of evolutionary algorithms can effectively be used. Is it possible to use for planning systems and for problem optimization? Why are they more favorable than other types of evolutionary algorithms? This work tries to answer these questions and explain the behavior of these algorithms.
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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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GUI for Handling Genetic Programming Chromozome
Staurovská, Jana ; Žaloudek, Luděk (referee) ; Jaroš, Jiří (advisor)
The main goal of this thesis is to create a program for manipulation with genetic programming chromosomes, which should allow export to a vector graphics format, moving of gates, their colouring and other graphical operations, and will work on different operating systems (mainly Microsoft Windows and Linux). For better understanding, the basic principles of cartesian genetic programming are described in theoretical part.
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Evolutionary Design of Image Classifier
Koči, Martin ; Bidlo, Michal (referee) ; Drahošová, Michaela (advisor)
This thesis deals with evolutionary design of image classifier with help of genetic programming, specifically with cartesian genetic programming. Thesis discribes teoretical basics of machine learing, evolutionary algorithms and genetic programming. Part of this thesis is described design of the program and its implementation. Futhermore, experiments are performed on two solved tasks for the classification of handwritten digits and the classification of cube drawings, which can be used to determine the rate of dementia in Parkinson's disease. The best designed solution for digits is with AUC of 0.95 and for cubes 0.86. Designed solutions are compared by other methods, namely convolutional neural networks (CNN) and the support vector machines (SVM). The resulting AUC for the classification of digits for both CNN and SVM is 0.99, for cubes CNN has a final AUC 0.81 and SVM 0.69. The cubes are then compared with existing solution, which resulted in AUC 0.70, so that the results of the experiments show an improvement in the method used in this thesis.
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Genetic Programming for Design of Digital Circuits
Hejtmánek, Michal ; Bidlo, Michal (referee) ; Gajda, Zbyšek (advisor)
The goal of this work was the study of evolutionary algorithms and utilization of them for digital circuit design. Especially, a genetic programming and its different manipulation with building blocks is mentioned in contrast to a genetic algorithm. On the basis of this approach, I created and tested a hybrid method of electronic circuit design. This method uses spread schemes according to the genetic algorithm for the pattern problems witch are solved by the genetic programming. The method is more successful and have faster convergence to a solution in difficult electronic circuits design than a common algorithm of the genetic programming.
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