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Competitive Coevolution in Cartesian Genetic Programming
Skřivánková, Barbora ; Petrlík, Jiří (referee) ; Drahošová, Michaela (advisor)
Symbolic regression is a function formula search approach dealing with isolated points of the function in plane or space. In this thesis, the symbolic regression is performed by Cartesian Genetic Programming and Competitive Coevolution. This task has already been resolved by Cartesian Genetic Programming using Coevolution of Fitness Predictors. This thesis is concerned with comparison of Coevolution of Fitness Predictors with simpler Competitive Coevolution approach in terms of approach effort. Symbolic regression has been tested on five functions with different complexity. It has been shown, that Competitive Coevolution accelerates the symbolic regression task on plainer functions in comparison with Coevolution of Fitness Predictors. However, Competitive Coevolution is not able to solve more complex functions in which Coevolution of Fitness Predictors succeeded.
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Evolutionary Algorithms for Neural Networks Learning
Vosol, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
Main point of this thesis is to find and compare posibilities of cooperation between evolutionary algorithms and neural network learning and their comparison with classical learning technique called backpropagation. This comparison is demonstrated with deep feed-forward neural network which is used for classification tasks. The process of optimalization is via search of optimal values of weights and biases within neural network with fixed topology. We chose three evolutionary approaches. Genetic algorithm, differential evolution and particle swarm optimization algorithm. These three approaches are also compared between each other. The demonstrating program is implemented in Python3 programming language without usage of any third parties libraries focused on deep learning.
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Coevolution of Image Filters and Fitness Predictors
Trefilík, Jakub ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This thesis deals with employing coevolutionary principles to the image filter design. Evolutionary algorithms are very advisable method for image filter design. Using coevolution, we can add the processes, which can accelerate the convergence by interactions of candidate filters population with population of fitness predictors. Fitness predictor is a small subset of the training set and it is used to approximate the fitness of the candidate solutions. In this thesis, indirect encoding is used for predictors evolution. This encoding represents a mathematical expression, which selects training vectors for candidate filters fitness prediction. This approach was experimentally evaluated in the task of image filters for various intensity of random impulse and salt and pepper noise design and the design of the edge detectors. It was shown, that this approach leads to adapting the number of target objective vectors for a particular task, which leads to computational complexity reduction.
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Evolutionary Design of Combinational Circuits on Computer Cluster
Pánek, Richard ; Zachariášová, Marcela (referee) ; Hrbáček, Radek (advisor)
This master's thesis deals with evolutionary algorithms and how them to use to design of combinational circuits. Genetic programming especially CGP is the most applicable to use for this type of task. Furthermore, it deals with computation on computer cluster and the use of evolutionary algorithms on them. For this computation is the most suited island models with CGP. Then a new way of recombination in CGP is designed to improve them. This design is implemented and tested on the computer cluster.
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Evolutionary algorithms
Haupt, Daniel ; Polách, Petr (referee) ; Honzík, Petr (advisor)
The first part of this work deals with the optimization and evolutionary algorithms which are used as a tool to solve complex optimization problems. The discussed algorithms are Differential Evolution, Genetic Algorithm, Simulated Annealing and deterministic non-evolutionary algorithm Taboo Search.. Consequently the discussion is held on the issue of testing the optimization algorithms through the use of the test function gallery and comparison solution all algorithms on Travelling salesman problem. In the second part of this work all above mentioned optimization algorithms are tested on 11 test functions and on three models of placement cities in Travelling salesman problem. Firstly, the experiments are carried out with unlimited number of accesses to the fitness function and secondly with limited number of accesses to the fitness function. All the data are processed statistically and graphically.
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Aplikace evolučního algoritmu na optimalizační úlohu vibračního generátoru
Nguyen, Manh Thanh ; Kovář, Jiří (referee) ; Hadaš, Zdeněk (advisor)
This thesis will deal with the use of artificial intelligence methods for solving optimization problems with multiple variables. A theorethical part presents problems of global optimization and overview of solution methods. For practical reasons, special attention is paid to evolutionary algorithms. The subject of optimization itself is energy harvester based on a piezoelectric effect. Its nature and modeling is devoted to one chapter. A part of the thesis is the implementation of the SOMA algorithm for finding the optimal parameters of the generator for maximum performance.
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Jumping Formal Models Inference
Heindlová, Tina ; Bidlo, Michal (referee) ; Křivka, Zbyněk (advisor)
This thesis is focused on grammatical inference in the way of evolutionary algorithms for jumping finite models. The first part explains jumping finite models as itself. More specifically, it describes jumping grammars and jumping automata. The next part deals with grammatical inference, evolutionary algorithms, and their important parts. According to the developed jumping models, said parts include strings generation and membership testing. These two algorithms are applied to chosen types of jumping finite automata---jumping finite automata, general jumping finite automata, and right one-way jumping finite automata. These four types of automata were tested, and in total, sixteen experiments were run. Results show that the inference works much better for automata without branching and with a small number of states and a small alphabet.
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Genetic Relatedness Analysis of Approximate Circuits
Krejčík, Vojtěch ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
The goal of this thesis is analyzing a large library of approximate circuits (EvoApproxLib) which was created using an evolutionary algorithm and used as a source of genetic data for the purposes of this thesis. More specifically it is a relatedness search in a file containing 24 912 8-bit approximate multipliers which were created by evolution from six different fully functioning parent implementations of multiplication. Gate counts and existence of 16 specific subcircuits were used as relatedness indicators. Various classifiers for assigning multipliers to one of six classes corresponding to parent implementations were trained based on these indicators. A classification success rate of up to 77% was achieved using said indicators. The results of this work show that combinations of specific subcircuits are a strong indicator for identifying which parent circuit the given approximate circuit comes from.
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