scholarly journals Pruning Techniques for Mixed Ensembles of Genetic Programming Models

2018 ◽  
pp. 52-67 ◽  
Author(s):  
Mauro Castelli ◽  
Ivo Gonçalves ◽  
Luca Manzoni ◽  
Leonardo Vanneschi
Keyword(s):  
Genetic Programming ◽  
Programming Models ◽  
Pruning Techniques

Can clustering improve glucose forecasting with genetic programming models?

2019 ◽  
Author(s):  
Sergio Contador ◽  
J. Ignacio Hidalgo ◽  
Oscar Garnica ◽  
J. Manuel Velasco ◽  
Juan Lanchares
Keyword(s):  
Genetic Programming ◽  
Programming Models

COMPUTER-AIDED PREDICTION OF PHYSICAL AND MECHANICAL PROPERTIES OF HIGH STRENGTH CEMENTITIOUS COMPOSITE CONTAINING Cr2O3 NANOPARTICLES

NANO ◽  
2010 ◽  
Vol 05 (05) ◽  
pp. 301-318 ◽  
Author(s):  
ALI NAZARI ◽  
SHADI RIAHI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Flexural Strength ◽  
Genetic Programming ◽  
Water Absorption ◽  
Physical And Mechanical Properties ◽  
High Strength ◽  
Programming Models ◽  
Input Parameters ◽  
Percentage Of Water Absorption

In the present paper, two models based on artificial neural networks (ANN) and genetic programming (GEP) for predicting flexural strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models the flexural strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the flexural strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

scholarly journals Estimation of suspended sediment yield flowing into Inanda Dam using genetic programming

2016 ◽  
Author(s):  
◽  
Adesoji Tunbosun Jaiyeola
Keyword(s):  
Genetic Programming ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Economic Life ◽  
Programming Models ◽  
Programming Approach ◽  
Suspended Sediment Load ◽  
Predictive Tool ◽  
Programming Technique ◽  
Rating Curves

Reservoirs are designed to specific volume called the dead storage to be able to withstand the quantity of particles in the rivers flowing into it during its design period called its economic life. Therefore, accurate calculation of the quantities of sediment being transported is of great significance in environment engineering, hydroelectric equipment longevity, river aesthetics, pollution and channel navigability. In this study different input combination of monthly upstream suspended sediment concentration and upstream flow dataset for Inanda Dam for 15 years was used to develop a model for each month of the year. The predictive abilities of each of the developed model to predict the quantity of suspended sediment flowing into Inanda Dam were also compared with those of the corresponding developed Sediment Rating Curves using two evaluation criteria - Determination of Coefficient (R2) and Root-Mean-Square Error (RMSE). The results from this study show that a genetic programming approach can be used to accurately predict the relationship between the streamflow and the suspended sediment load flowing into Inanda Dam. The twelve developed monthly genetic programming (GP) models produced a significantly low difference when the observed suspended sediment load was compared with the predicted suspended sediment load. The average R2 values and RMS error for the twelve developed models were 0.9996 and 0.3566 respectively during the validation phase. The Genetic Programming models were also able to replicate extreme hydrological events like predicting low and high suspended sediment load flowing into the dam. Moreover, the study also produced accurate sediment rating curve models with low RMSE values of between 0.3971 and 11.8852 and high R2 values of between 0.9833 and 0.9962. This shows that sediment rating curves can be used to predict historical missing data of the quantity of suspended sediment flowing into Inanda Dam using existing streamflow datasets. The results from this study further show that the predictions from the Genetic Programming models are better than the predictions from the Sediment Raring Curve models, especially in predicting large quantities of suspended sediment load during high streamflow such as during flood events. This proves that Genetic Programming technique is a better predictive tool than Sediment Raring Curve technique. In conclusion, the results from this study are very promising and support the use of Genetic Programming in predicting the nonlinear and complex relationship between suspended sediment load and streamflow at the inlet of Inanda Dam in KwaZulu-Natal. This will help planners and managers of the dam to understand the system better in terms of its problems and to find alternative ways to address them.

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