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Cryptanalysis of Symmetric Encryption Algorithms Using Genetic Programming
Smetka, Tomáš ; Janoušek, Vladimír (referee) ; Homoliak, Ivan (advisor)
This diploma thesis deals with the cryptanalysis of symmetric encryption algorithms. The aim of this thesis is to show different point of view on this issues. The dissimilar way, compared to the recent methods, lies in the use of the power of evolutionary principles which are in the cryptanalytic system applied with help of genetic programming. In the theoretical part the cryptography, cryptanalysis of symmetric encryption algorithms and genetic programming are described. On the ground of the obtained information a project of cryptanalytic system which uses evolutionary principles is represented. Practical part deals with implementation of symmetric encrypting algorithm, linear cryptanalysis and simulation instrument of genetic programming. The end of the thesis represents experiments together with projected cryptanalytic system which uses genetic programming and evaluates reached results.
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Coevolutionary Algorithm for Test-Based Problems
Hulva, Jiří ; Sekanina, Lukáš (referee) ; Drahošová, Michaela (advisor)
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.
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Estimation of Algorithm Execution Time Using Machine Learning
Buchta, Martin ; Chlebík, Jakub (referee) ; Jaroš, Jiří (advisor)
This work aims to predict the execution time of k-Wave ultrasound simulations on supercomputers based on a given domain size. The program uses MPI and can be run on multiple nodes. Prediction models were developed using symbolic regression and neural networks, both of which trained on captured data and compared against each other. The results demonstrate that the models outperform existing solutions. Specifically, the symbolic regression model achieved an average error of 5.64% for suitable tasks, while the neural network model achieved an average error of 8.25% on unseen domain sizes and across all tasks, including those not optimized for k-Wave simulations. This work contributes a new, more accurate model for predicting execution time, and compares the effectiveness of neural networks and symbolic regression for this specific type of regression problem. Overall, these findings suggest that new models will have important practical applications in the field of k-Wave ultrasound simulations.
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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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Indexing Arbitrary Similarity Models
Bartoš, Tomáš ; Skopal, Tomáš (advisor) ; Bustos, Benjamin (referee) ; Dohnal, Vlastislav (referee)
The performance of similarity search in the unstructured databases largely depends on the employed similarity model. The properties of metric space model enable indexing the data with metric access methods efficiently. But for unconstrained or nonmetric similarity models typical for multimedia, medical, or scientific databases, in which metric postulates do not hold, there exists no general solution so far. Motivated by the successful application of Ptolemaic indexing to the image retrieval, we introduce SIMDEX Framework which is a universal framework that is capable of revealing alternative indexing methods that will serve for efficient yet effective similarity searching for any similarity model. It explores the axiom space in order to discover novel techniques suitable for database indexing. We review all existing variants (simple I-SIMDEX; GP-SIMDEX and PGP-SIMDEX which both use genetic programming) and we outline how the different groups of domain researchers can benefit from them. We also describe a real application of SIMDEX Framework to practice while building the Smart Pivot Table indexing method together with advanced Triangle+ filtering for metric spaces empowered by LowerBound Tightening technique. At all cases, we provide extensive experimental evaluations of mentioned techniques. Powered by...
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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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Genetické programování a jeho praktické využití
DIBITANZL, Jaroslav
This bachelor's thesis deals with symbolic regression and its solution using genetic programming. The thesis consists of theoretical a practical part. Theoretical part focuses on principle of genetic programming, practical part contains example solution of symbolic regression using libraries Clojush, EllenGP, DEAP, FlexGP, KarooGP and describes own solution. Goal of this thesis is to display possibilities of genetic programming and how it can be used for solving symbolic regression. Outcomes of thesis are examples of individual above-mentioned libraries and analyse own solution of symbolic regression.
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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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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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Indexing Arbitrary Similarity Models
Bartoš, Tomáš ; Skopal, Tomáš (advisor) ; Bustos, Benjamin (referee) ; Dohnal, Vlastislav (referee)
The performance of similarity search in the unstructured databases largely depends on the employed similarity model. The properties of metric space model enable indexing the data with metric access methods efficiently. But for unconstrained or nonmetric similarity models typical for multimedia, medical, or scientific databases, in which metric postulates do not hold, there exists no general solution so far. Motivated by the successful application of Ptolemaic indexing to the image retrieval, we introduce SIMDEX Framework which is a universal framework that is capable of revealing alternative indexing methods that will serve for efficient yet effective similarity searching for any similarity model. It explores the axiom space in order to discover novel techniques suitable for database indexing. We review all existing variants (simple I-SIMDEX; GP-SIMDEX and PGP-SIMDEX which both use genetic programming) and we outline how the different groups of domain researchers can benefit from them. We also describe a real application of SIMDEX Framework to practice while building the Smart Pivot Table indexing method together with advanced Triangle+ filtering for metric spaces empowered by LowerBound Tightening technique. At all cases, we provide extensive experimental evaluations of mentioned techniques. Powered by...
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