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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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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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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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Cartesian Genetic Programming in Python
Dvořáček, Petr ; Bidlo, Michal (referee) ; Vašíček, Zdeněk (advisor)
Cartesian genetic programming (CGP) is one of the evolutionary methods. It was created for electronic circuit design. It can be used also in optimization of functions, classification, evolutionary art etc. This paper describes acceleration techniques to speed up the evaluation of candidate solution in CGP in Python.
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Utilization of Evolutionary Algorithms in Symbolic Regression Problem
Komadel, Michal ; Slaný, Karel (referee) ; Vašíček, Zdeněk (advisor)
Evolutionary algorithms are constantly developing and progressive part of informatics. These algorithms serve to solve many kinds of problems from optimal control to planning. This study discusses genetic and cartesian genetic programming, which belong among the most successful types of evolutionary algorithms. The goal of this work is to develop two aplications of genetic and cartesian genetic programming and evaluate efficiency of these two types of evolutionary algorithms in solving symbolic regression problems.
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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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Regession Methods in Traffic Prediction
Vaňák, Tomáš ; Korček, Pavol (referee) ; Petrlík, Jiří (advisor)
Master thesis deals with possibilities of predicting traffic situation on the macroscopic level using data, that were recorded using traffic sensors. This sensors could be loop detectors, radar detectors or cameras. The main problem discussed in this thesis is the travel time of cars. A method for travel time prediction was designed and implemented as a part of this thesis. Data from real traffic were used to test the designed method. The first objective of this thesis is to become familiar with the prediction methods that will be used. The main objective is to use the acquired knowledge to design and to implement an aplication that will predict required traffic variables.
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