Object categorization using Cartesian Genetic Programming (CGP) and CGP-Evolved Artificial Neural Network

2010 ◽  
Author(s):  
Sumayyea Salahuddin ◽  
Maryam Mahsal Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Programming ◽  
Object Categorization ◽  
Cartesian Genetic Programming ◽  
Artificial Neural

Concrete strength prediction using artificial neural network and genetic programming

2018 ◽  
Vol 9 (3) ◽  
pp. 75
Author(s):  
Preeti Kulkarni ◽  
Shreenivas N. Londhe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Programming ◽  
Construction Material ◽  
Concrete Strength ◽  
Maximum Size ◽  
Design Parameters ◽  
Ann Model ◽  
Influencing Parameters ◽  
Artificial Neural

Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.

scholarly journals Application of Artificial Neural Network and Genetic Programming in Civil Engineering

2016 ◽  
pp. 1022-1036
Author(s):  
Pijush Samui ◽  
Dhruvan Choubisa ◽  
Akash Sharda
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Programming ◽  
Input Parameter ◽  
Air Entrainment ◽  
Generalized Regression Neural Network ◽  
Entrainment Rate ◽  
Perfect Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This chapter examines the capability of Genetic Programming (GP) and different Artificial Neural Network (ANN) (Backpropagation [BP] and Generalized Regression Neural Network [GRNN]) models for prediction of air entrainment rate (QA) of triangular sharp-crested weir. The basic principal of GP has been taken from the concept of Genetic Algorithm (GA). Discharge (Q), drop height (h), and angle in triangular sharp-crested weir (?) are considered as inputs of BP, GRNN, and GP. Coefficient of Correlation (R) has been used to assess the performance of developed GP, BP, and GRNN models. For a perfect model, the value of R should be close to one. A sensitivity analysis has been carried out to determine the effect of each input parameter. This chapter presents a comparative study between the developed BP, GRNN, and GP models.

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