Genetic Programming-based Model-building Attack on PUFs

2014 ◽  
pp. 529-538
Keyword(s):  
Genetic Programming ◽  
Model Building

scholarly journals A Study On Financial Time Series Forecasting And Symbolic Regression By Means Of A Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Mahsa Mostowfi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Genetic Programming ◽  
Probabilistic Model ◽  
Stock Price ◽  
Model Building ◽  
Symbolic Regression ◽  
Time Series Forecasting ◽  
Fusion Model ◽  
Regression Problems

This work proposes a hybrid algorithm called Probabilistic Incremental Cartesian Genetic Pro- gramming (PI-CGP), which integrates an Estimation of Distribution Algorithm (EDA) with Carte- sian Genetic Programming (CGP). PI-CGP uses a fixed-length problem representation and the algorithm constructs a probabilistic model of promising solutions. PI-CGP was evaluated on sym- bolic regression problems and next trading day stock price forecasting. On the symbolic regression problems PI-CGP did not outperform other approaches. The reason could be premature convergence and being trapped at a local minimum. However, PI-CGP was competitive at stock market forecasting. It was comparable to a fusion model employing a Hidden Markov Model (HMM). HMMs are extensively used for time-series forecasting. This result is promising considering the volatile nature of the stock market and that PI-CGP was not customized toward forecasting.

scholarly journals A Study On Financial Time Series Forecasting And Symbolic Regression By Means Of A Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Mahsa Mostowfi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Genetic Programming ◽  
Probabilistic Model ◽  
Stock Price ◽  
Model Building ◽  
Symbolic Regression ◽  
Time Series Forecasting ◽  
Fusion Model ◽  
Regression Problems

This work proposes a hybrid algorithm called Probabilistic Incremental Cartesian Genetic Pro- gramming (PI-CGP), which integrates an Estimation of Distribution Algorithm (EDA) with Carte- sian Genetic Programming (CGP). PI-CGP uses a fixed-length problem representation and the algorithm constructs a probabilistic model of promising solutions. PI-CGP was evaluated on sym- bolic regression problems and next trading day stock price forecasting. On the symbolic regression problems PI-CGP did not outperform other approaches. The reason could be premature convergence and being trapped at a local minimum. However, PI-CGP was competitive at stock market forecasting. It was comparable to a fusion model employing a Hidden Markov Model (HMM). HMMs are extensively used for time-series forecasting. This result is promising considering the volatile nature of the stock market and that PI-CGP was not customized toward forecasting.

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