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Využití regresních metod pro predikci dopravy / Regession Methods in Traffic PredictionVaňák, Tomáš January 2014 (has links)
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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Genetické vylepšení software pro kartézské genetické programování / Genetic Improvement of Cartesian Genetic Programming SoftwareHusa, Jakub January 2016 (has links)
Genetic programming is a nature-inspired method of programming that allows an automated creation and adaptation of programs. For nearly two decades, this method has been able to provide human-comparable results across many fields. This work gives an introduction to the problems of evolutionary algorithms, genetic programming and the way they can be used to improve already existing software. This work then proposes a program able to use these methods to improve an implementation of cartesian genetic programming (CGP). This program is then tested on a CGP implementation created specifically for this project, and its functionality is then verified on other already existing implementations of CGP.
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A multi-gene symbolic regression approach for predicting LGD : A benchmark comparative studyTuoremaa, Hanna January 2023 (has links)
Under the Basel accords for measuring regulatory capital requirements, the set of credit risk parameters probability of default (PD), exposure at default (EAD) and loss given default (LGD) are measured with own estimates by the internal rating based approach. The estimated parameters are also the foundation of understanding the actual risk in a banks credit portfolio. The predictive performance of such models are therefore interesting to examine. The credit risk parameter LGD has been seen to give low performance for predictive models and LGD values are generally hard to estimate. The main purpose of this thesis is to analyse the predictive performance of a multi-gene genetic programming approach to symbolic regression compared to three benchmark regression models. The goal of multi-gene symbolic regression is to estimate the underlying relationship in the data through a linear combination of a set of generated mathematical expressions. The benchmark models are Logit Transformed Regression, Beta Regression and Regression Tree. All benchmark models are frequently used in the area. The data used to compare the models is a set of randomly selected, de-identified loans from the portfolios of underlying U.S. residential mortgage-backed securities retrieved from International Finance Research. The conclusion from implementing and comparing the models is that, the credit risk parameter LGD is continued difficult to estimated, the symbolic regression approach did not yield a better predictive ability than the benchmark models and it did not seem to find the underlying relationship in the data. The benchmark models are more user-friendly with easier implementation and they all requires less calculation complexity than symbolic regression.
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Data-Driven Models for Infrastructure Climate-Induced Deterioration PredictionElleathy, Yasser January 2021 (has links)
Infrastructure deterioration has been attributed to insufficient maintenance budgets, lacking restoration strategies, deficient deterioration prediction techniques, and changing climatic conditions. Considering that the latter adds more challenges to the former, there has been a growing demand to develop and implement climate-informed infrastructure asset management strategies. However, quantifying the impact of the spatiotemporally varying climate metrics on infrastructure systems poses a serious challenge due to the associated complexities and relevant modelling uncertainties. As such, in lieu of complex physics-based simulations, the current study proposes a glass box data-driven framework for predicting infrastructure climate induced deterioration rates. The framework harnesses evolutionary computing, and specifically multigene genetic programming, to develop closed-form expressions that link infrastructure characteristics to relevant spatiotemporal climate indices and predict infrastructure deterioration rates. The framework consists of four steps: 1) data collection and preparation; 2) input integration; 3) feature selection; and 4) model development and result interpretation. To numerically demonstrate its utility, the proposed framework was applied to develop deterioration rate expressions of two different classes of concrete and steel bridges in Ontario, Canada. The developed predictive models reproduced the observed deterioration rate of both bridge classes with coefficient of determination (R2) values of 0.912 and 0.924 for the training subsets and 0.817 and 0.909 for the testing subsets of the concrete and steel bridges, respectively. Attributed to its generic nature, the framework can be applied to other infrastructure systems, with available historical deterioration data, to devise relevant effective asset management strategies and infrastructure restoration standards under future climate scenarios. / Thesis / Master of Applied Science (MASc)
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Indexovanie podobnostných modelov / Indexing Arbitrary Similarity ModelsBartoš, Tomáš January 2014 (has links)
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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Previsão de cargas elétricas a curto prazo por combinação de previsões via regressão simbólicaBraga, Douglas de Oliveira Matos 31 August 2017 (has links)
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Previous issue date: 2017-08-31 / O planejamento energético é base para as tomadas de decisões nas companhias de energia
elétrica e, para isto, depende fortemente da disponibilidade de previsões acuradas para as
cargas. Devido á inviabilidade de armazenamentos em larga-escala e o custo elevado de compras de energia a curto prazo, além da possibilidade de multas e sanções de órgãos governamentais, previsões em curto prazo são importantes para a otimização da alocação de recursos e da geração de energia.
Neste trabalho utilizamos nove métodos univariados de séries temporais para a
previsão de cargas a curto prazo, com horizontes de 1 a 24 horas a frente. Buscando melhorar a acurácia das previsões, propomos um método de combinação de previsões através de Regressão Simbólica, que combina de forma não-linear as previsões obtidas pelos nove métodos de séries temporais utilizados. Diferente de outros métodos não-lineares
de regressão, a Regressão Simbólica não precisa de uma especificação previa da forma funcional.
O método proposto é aplicado em uma série real da cidade do Rio de Janeiro (RJ), que contém cargas horárias de 104 semanas dos anos de 1996 e 1997. Comparamos, através de critérios indicados na literatura, os resultados obtidos pelo método proposto com os resultados obtidos por métodos tradicionais de combinação de previsões e ao resultado de simulações de redes neurais artificiais aplicados ao mesmo conjunto de dados. O método proposto obteve melhores resultados, que indicam que a não-linearidade pode ser aspecto importante para combinação de previsões no problema de previsão de carga a curto prazo / Decision-making in energy companies relies heavily on the availability of accurate load
forecasts. Because storing electricity on a large scale is not viable, the cost of short-term
energy purchasing is high, and there are government fines and sanctions for failing to
supply energy on demand, short-term load forecasts are important for the optimization
of resource allocation and energy production.
In this work we used nine univariate time series methods for short-term load forecasts,
with forecast horizons ranging from 1 to 24 hours ahead. In order to improve the accuracy
of forecasts, we propose a method of combining forecasts through Symbolic Regression,
which combines in a non-linear way the forecasts obtained by the nine methods of the
time series used. Unlike other non-linear regression methods, Symbolic Regression does
not need a previous specification of the function structure.
We applied the proposed method to a real time series of the city of Rio de Janeiro (RJ),
which contains data on hourly loads of 104 weeks in the years 1996 and 1997. We compare,
through the criteria indicated in the literature, the results obtained by the proposed
method with the results obtained by traditional methods of forecasts combination and
the result obtained by artificial neural networks applied to the same dataset. The method
has yielded better results, indicating that non-linearity may be important in combining
predictions in short term load forecasts.
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Geometrické sémantické genetické programování / Geometric Semantic Genetic ProgrammingKončal, Ondřej January 2018 (has links)
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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Možnosti akcelerace symbolické regrese pomocí kartézského genetického programování / Acceleration of Symbolic Regression Using Cartesian Genetic ProgrammingHodaň, David January 2019 (has links)
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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Symbolická regrese a koevoluce / Symbolic Regression and CoevolutionDrahošová, Michaela January 2011 (has links)
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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[en] INFERENCE OF THE ANALYTICAL EXPRESSION FROM AN OPTIMAL INVESTMENT BOUNDARY FOR AN ASSET THAT FOLLOWS THE REVERSION MEAN PROCESS THROUGH GENETIC PROGRAMMING / [pt] INFERÊNCIA DA EXPRESSÃO ANALÍTICA DE UMA FRONTEIRA DE INVESTIMENTO ÓTIMO PARA UM ATIVO QUE SEGUE O PROCESSO DE REVERSÃO À MÉDIA POR PROGRAMAÇÃO GENÉTICADAN POSTERNAK 21 December 2004 (has links)
[pt] Esta Pesquisa tem por objetivo utilizar a Regressão
Simbólica por
Programação Genética para encontrar uma equação analítica
para a fronteira de
exercício ótima (ou curva de gatilho) de uma opção sobre
um
ativo do qual o
preço tem um comportamento simulado pelo processo
estocástico conhecido
como processo de reversão à média (PRM).
Para o cálculo do valor de uma opção desde de sua
aquisição
até sua
maturação, normalmente faz-se o uso do cálculo da
fronteira
de exercício
ótimo. Esta curva separa ao longo do tempo a decisão de
exercer ou não a
opção.
Sabendo-se que já existem soluções analíticas para
calcular
a fronteira de
exercício ótimo quando o preço do ativo segue um
Movimento
Geométrico
Browniano, e que tal solução genérica ainda não foi
encontrada para o PRM,
neste trabalho, foi proposto o uso da Programação
Genética
(PG) para encontrar
tal solução analítica.
A Programação Genética utilizou um conjunto de amostras
de
curvas de
exercício ótimo parametrizadas segundo a variação da
volatilidade e da taxa de
juros livre de risco, para encontrar uma função analítica
para a fronteira de
exercício ótima, obtendo-se resultados satisfatórios. / [en] This research intends on to use the Symbolic Regression by
Genetic
Programming to find an analytical equation that represents
an Optimal Exercise
Boundary for an option of an asset having its price
behavior simulated by a
stochastic process known as Mean Reversion Process (MRP).
To calculate an option value since its acquisition until
its maturity,
normally is used to calculate the Optimal Exercise
Boundary. This frontier
separates along the time the decision to exercise the
option or not.
Knowing there already are analytical solutions used to
calculate the
Optimal Exercise Boundary when the asset price follows the
Geometric
Brownian Motion, and such general solution was not found
yet to MRP, in this
work, it was proposed the use of Genetic Programming to
find such analytical
solution.
The Genetic Programming used an amount of samples from
optimal
exercise curves parameterized according the change in the
volatility and risk
free interest rate, to find an analytical function that
represents Optimal Exercise
Boundary, achieving satisfactory results.
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