A Genetic Representation for Dynamic System Qualitative Models on Genetic Programming: A Gene Expression Programming Approach

2007 ◽  
pp. 30-40
Author(s):  
Ramiro Serrato Paniagua ◽  
Juan J. Flores Romero ◽  
Carlos A. Coello Coello
Keyword(s):  
Gene Expression ◽  
Genetic Programming ◽  
Dynamic System ◽  
Gene Expression Programming ◽  
Programming Approach ◽  
Qualitative Models

Gene Expression Programming

2017 ◽  
pp. 269-292 ◽  
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

scholarly journals Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

2019 ◽  
Vol 06 (02) ◽  
pp. 163-175 ◽  
Author(s):  
Joanna Jȩdrzejowicz ◽  
Piotr Jȩdrzejowicz ◽  
Izabela Wierzbowska
Keyword(s):  
Gene Expression ◽  
Parallel Processing ◽  
Genetic Programming ◽  
Open Source ◽  
Gene Expression Programming ◽  
Ensemble Classifier ◽  
Computational Experiments ◽  
Programming Library ◽  
Benchmark Datasets ◽  
Parallel Environment

The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.

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.

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

Gene Expression Programming

2021 ◽  
pp. 427-446
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

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