A genetic programming model for bankruptcy prediction: Empirical evidence from Iran

2009 ◽  
Vol 36 (2) ◽  
pp. 3199-3207 ◽  
Author(s):  
Hossein Etemadi ◽  
Ali Asghar Anvary Rostamy ◽  
Hassan Farajzadeh Dehkordi
Keyword(s):  
Genetic Programming ◽  
Empirical Evidence ◽  
Programming Model ◽  
Bankruptcy Prediction

Genetic programming model for forecast of short and noisy data

2007 ◽  
Vol 21 (2) ◽  
pp. 266-272 ◽  
Author(s):  
C. Sivapragasam ◽  
P. Vincent ◽  
G. Vasudevan
Keyword(s):  
Genetic Programming ◽  
Programming Model ◽  
Noisy Data

A new genetic programming model for predicting settlement of shallow foundations

2007 ◽  
Vol 44 (12) ◽  
pp. 1462-1473 ◽  
Author(s):  
Mohammad Rezania ◽  
Akbar A. Javadi
Keyword(s):  
Genetic Programming ◽  
Programming Model ◽  
Standard Penetration Test ◽  
Shallow Foundations ◽  
Penetration Test ◽  
Case Histories ◽  
Contributing Factors ◽  
Artificial Neural Network Ann ◽  
Gp Model ◽  
Very High

In this paper, a new genetic programming (GP) approach for predicting settlement of shallow foundations is presented. The GP model is developed and verified using a large database of standard penetration test (SPT) based case histories that involve measured settlements of shallow foundations. The results of the developed GP model are compared with those of a number of commonly used traditional methods and artificial neural network (ANN) based models. It is shown that the GP model is able to learn, with a very high accuracy, the complex relationship between foundation settlement and its contributing factors, and render this knowledge in the form of a function. The attained function can be used to generalize the learning and apply it to predict settlement of foundations for new cases not used in the development of the model. The advantages of the proposed GP model over the conventional and ANN based models are highlighted.

Genetic Programming Model Regularization

2015 ◽  
pp. 105-120
Author(s):  
César L. Alonso ◽  
José Luis Montaña ◽  
Cruz Enrique Borges
Keyword(s):  
Genetic Programming ◽  
Programming Model

A heating value estimation of refuse derived fuel using the genetic programming model

2019 ◽  
Vol 100 ◽  
pp. 327-335 ◽  
Author(s):  
Kemal Özkan ◽  
Şahin Işık ◽  
Zerrin Günkaya ◽  
Aysun Özkan ◽  
Müfide Banar
Keyword(s):  
Genetic Programming ◽  
Programming Model ◽  
Heating Value ◽  
Refuse Derived Fuel ◽  
Value Estimation

scholarly journals Ensemble modeling approach for rainfall/groundwater balancing

2007 ◽  
Vol 9 (2) ◽  
pp. 95-106 ◽  
Author(s):  
D. Laucelli ◽  
O. Giustolisi ◽  
V. Babovic ◽  
M. Keijzer
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Empirical Evidence ◽  
Averaging Method ◽  
Total Error ◽  
Ensemble Methods ◽  
Real Data ◽  
Symbolic Regression ◽  
Ensemble Modeling ◽  
Physical Phenomena

This paper introduces an application of machine learning, on real data. It deals with Ensemble Modeling, a simple averaging method for obtaining more reliable approximations using symbolic regression. Considerations on the contribution of bias and variance to the total error, and ensemble methods to reduce errors due to variance, have been tackled together with a specific application of ensemble modeling to hydrological forecasts. This work provides empirical evidence that genetic programming can greatly benefit from this approach in forecasting and simulating physical phenomena. Further considerations have been taken into account, such as the influence of Genetic Programming parameter settings on the model's performance.

scholarly journals Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95333-95344
Author(s):  
Hang Yao ◽  
Xiang Jia ◽  
Qian Zhao ◽  
Zhi-Jun Cheng ◽  
Bo Guo
Keyword(s):  
Genetic Programming ◽  
Lithium Ion Battery ◽  
Programming Model ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Health
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