A Gene Expression Programming Approach for Vehicle Body Segmentation and Color Recognition

2020 ◽  
Author(s):  
Brenda Cinthya Solari Berno ◽  
Lucas Augusto Albini ◽  
Vinícius Couto Tasso ◽  
César Manuel Vargas Benítez ◽  
Heitor Silvério Lopes
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Vehicle Body ◽  
Programming Approach ◽  
Color Recognition ◽  
Body Segmentation

Forecasting Volatility Returns of Oil Price Using Gene Expression Programming Approach

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Alexander Amo Baffour ◽  
Jingchun Feng ◽  
Liwei Fan ◽  
Beryl Adormaa Buanya
Keyword(s):  
Gene Expression ◽  
Moving Average ◽  
Gene Expression Programming ◽  
Price Volatility ◽  
Oil Price ◽  
Loss Functions ◽  
Programming Approach ◽  
Volatility Forecasting ◽  
Oil Price Volatility ◽  
Forecasting Volatility

AbstractThis study employs four (4) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants namely GARCH (1, 1), Glosten–Jagannathan–Runkle (GJR), Auto Regressive Integrated Moving Average (ARIMA)-GARCH and ARIMA-GJR as benchmark models to assess the performance of a proposed novel Gene Expression Programming (GEP) based univariate time series modeling approach used to conduct ex ante oil price volatility forecasts. The report illustrates that the GEP model is more superior to any of the traditional models on issues relating to both loss functions applied. The GEP model is of a greater volatility forecasting precision at different forecast horizons, therefore. There is also the existence of evidence that GJR and ARIMA-GJR differ in their loss functions, the performance is nevertheless better than GARCH (1, 1) and ARIMA-GARCH. This study conducted herein achieves importance in literature by broadening the application of gene algorithms in finance and forecasting. It also solves the problem of high error associated with the use of GARCH related models in oil price volatility forecasting.

An improved Gene Expression Programming approach for symbolic regression problems

2014 ◽  
Vol 137 ◽  
pp. 293-301 ◽  
Author(s):  
YuZhong Peng ◽  
ChangAn Yuan ◽  
Xiao Qin ◽  
JiangTao Huang ◽  
YaBing Shi
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Regression Problems
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