Probabilistic Model Building Genetic Programming based on Estimation of Bayesian Network

2007 ◽  
Vol 22 ◽  
pp. 37-47 ◽  
Author(s):  
Yoshihiko Hasegawa ◽  
Hitoshi Iba
Keyword(s):  
Genetic Programming ◽  
Bayesian Network ◽  
Probabilistic Model ◽  
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.

Probabilistic model building in genetic programming: a critical review

2013 ◽  
Vol 15 (2) ◽  
pp. 115-167 ◽  
Author(s):  
Kangil Kim ◽  
Yin Shan ◽  
Xuan Hoai Nguyen ◽  
R. I. McKay
Keyword(s):  
Genetic Programming ◽  
Probabilistic Model ◽  
Critical Review ◽  
Model Building

Manufacturing Quality Control Method Based on Bayesian Network Model

2010 ◽  
Vol 450 ◽  
pp. 292-295
Author(s):  
Ye Hong Dong ◽  
Dong Xiang ◽  
Guang Hong Duan
Keyword(s):  
Quality Control ◽  
Bayesian Network ◽  
Model Building ◽  
Control Method ◽  
Drilling Process ◽  
Quality Prediction ◽  
Shewhart Control ◽  
Drilling Quality ◽  
Micro Drilling ◽  
Manufacturing Mode

In order to address the problem of quality control faced in multi-type and small-batch manufacturing mode, the method based on Bayesian Network (BN) is proposed. The building, learning and evolving method as well as the quality prediction and diagnosis method of BN model are described in this paper. The combination of BN model and Shewhart control chart is also mentioned. The model building and evolving method was conducted in PCB micro-drilling process as example, verifying that the prediction accuracy increases with the evolved model. The drilling quality prediction was compared with that obtained through regression analysis and artificial neural network. The advantage of BN model in advanced manufacturing is proved.

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