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Využití operátoru křížení v kartézském genetickém programování
Bromnik, Petr ; Sekanina, Lukáš (referee) ; Hurta, Martin (advisor)
The aim of this paper is to propose and implement two new crossover methods in Cartesian Genetic Programming (CGP) and compare them with existing approaches. CGP is a type of evolutionary algorithm that uses acyclic graphs to represent executable programs. Most CGP applications use the mutation operator only, but the effort to find a suitable crossover operator is still ongoing. In this paper, the two newly proposed crossover methods are compared on five symbolic regression problems against the standard 1 + lambda procedure based purely on mutation. Experimental results show that these methods find solutions in a similar number of fitness evaluations as 1 + lambda and, in two cases, even significantly earlier.
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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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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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Grammatical Evolution in Software Optimization
Pečínka, Zdeněk ; Minařík, Miloš (referee) ; Sekanina, Lukáš (advisor)
This master's thesis offers a brief introduction to evolutionary computation. It describes and compares the genetic programming and grammar based genetic programming and their potential use in automatic software repair. It studies possible applications of grammar based genetic programming on automatic software repair. Grammar based genetic programming is then used in design and implementation of a new method for automatic software repair. Experimental evaluation of the implemented automatic repair was performed on set of test programs.
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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 computing
Popelka, Jan ; Smékal, Zdeněk (referee) ; Karásek, Jan (advisor)
The aim of this Bachelor's Thesis was to get acquainted with the Evolutionary Optimization Techniques, mainly with the Genetic Algorithm and Genetic Programming. It was subsequently described the role of optimization problem TSP solved using Genetic Algorithms and other Chapter solving Symbolic Regression using Genetic Programming. This optimalization problems were created in the programming JAVA and there are solved practical part of the thesis.
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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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Mutation in Cartesian Genetic Programming
Končal, Ondřej ; Hrbáček, Radek (referee) ; Wiglasz, Michal (advisor)
This thesis examines various kinds of mutations in the Cartesian Genetic Programming (CGP) on tasks of symbolic regression. The CGP is kind of evolutionary algorithm which operates with executable structures. Programs in CGP are evolved using mutation, which leads to offspring evaluation, which is the most time-consuming part of the algorithm. Finding more suitable kind of mutation can significantly accelerate the creation of new individuals and thus, reduce the time necessary to find a satisfactory solution. This thesis presents four different mutations for CGP. Experiments compare these mutation operators to solve five tasks of symbolic regression. Experiments have shown that a choice of suitable mutation can almost double the computing speed in comparison to the standard mutation.
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Geometric Semantic Genetic Programming
Končal, Ondřej ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
This thesis examines a conversion of a solution produced by geometric semantic genetic programming (GSGP) to an instantion of cartesian genetic programming (CGP). GSGP has proven its quality to create complex mathematical models; however, the size of these models can get problematically large. CGP, on the other hand, is able to reduce the size of given models. This thesis combinated these methods to create a subtree CGP (SCGP). The SCGP uses an output of GSGP as an input and the evolution is performed using the CGP. Experiments performed on four pharmacokinetic tasks have shown that the SCGP is able to reduce the solution size in every case. Overfitting was detected in one out of four test problems.
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Acceleration of Symbolic Regression Using Cartesian Genetic Programming
Hodaň, David ; Mrázek, Vojtěch (referee) ; Vašíček, Zdeněk (advisor)
This thesis is focused on finding procedures that would accelerate symbolic regressions in Cartesian Genetic Programming. It describes Cartesian Genetic Programming and its use in the task of symbolic regression. It deals with the SIMD architecture and the SSE and AVX instruction set. Several optimizations that lead to a significant acceleration of evolution in Cartesian Genetic Programming are presented. A method of a bit-level parallel simulation that uses AVX2 vectors allows to process 256 input combinations of a logic circuit in paralell. Similarly it is possible to use a byte-level parallel simulation and work with 32 bytes when evolving an image filter. A new method of batch mutation can accelerate the evolution of combinational logic circuits thousand times depending on the problem size. For example, using a combination of these and other methods the evolution of 5 x 5b multipliers took 5.8 seconds on average on an Intel Core i5-4590 processor.
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