Uniaxial compressive strength prediction through a new technique based on gene expression programming

2017 ◽  
Vol 30 (11) ◽  
pp. 3523-3532 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Vali Safari ◽  
Ahmad Fahimifar ◽  
Mohd For Mohd Amin ◽  
Masoud Monjezi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Gene Expression Programming ◽  
New Technique ◽  
Strength Prediction ◽  
A New Technique

Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

2016 ◽  
Vol 29 (11) ◽  
pp. 1115-1125 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Roohollah Shirani Faradonbeh ◽  
Hossein Rezaei ◽  
Ahmad Safuan A. Rashid ◽  
Hassan Bakhshandeh Amnieh
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
New Technique ◽  
Settlement Prediction ◽  
A New Technique

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.

A new predictive model for compressive strength of HPC using gene expression programming

2012 ◽  
Vol 45 (1) ◽  
pp. 105-114 ◽  
Author(s):  
Seyyed Mohammad Mousavi ◽  
Pejman Aminian ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi ◽  
Hamed Bolandi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Predictive Model ◽  
Gene Expression Programming

scholarly journals Gene-Expression Programming-Based Model for Estimating the Compressive Strength of Cement-Fly Ash Stabilized Soil and Parametric Study

2021 ◽  
Vol 6 (12) ◽  
pp. 181
Author(s):  
Van-Ngoc Pham ◽  
Erwin Oh ◽  
Dominic E. L. Ong
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Fly Ash ◽  
Calcium Oxide ◽  
Parametric Study ◽  
Soil Stabilization ◽  
Gene Expression Programming ◽  
Superior Performance ◽  
Stabilized Soil ◽  
Research Findings

The study aims to develop a reliable model using gene-expression programming (GEP) technique for estimating the unconfined compressive strength (UCS) of soil stabilization by cement and fly ash. The model considered the effects of several parameters, including the fly ash characteristics such as calcium oxide (CaO) content, CaO/SiO2 ratio, and loss of ignition. The research results show that the proposed model demonstrates superior performance with a high correlation coefficient (R > 0.955) and low errors. Therefore, the model could be confidently applied in practice for a variety of fly ash qualities. Besides, the parametric study was conducted to examine the effect of fly ash characteristics on the strength of soil stabilization. The study indicates that if the fly ash contains a high amount of calcium oxide, the strength of fly ash stabilized soil is significant. In addition, fly ash could be used in combination with cement to increase the strength of the mixture. A fly ash replacement ratio is suggested from 0.19 to 0.35, corresponding to the total binder used from 10% to 30%. The research findings could help engineers in optimizing the fly ash proportion and estimating the UCS of soil stabilization by cement and fly ash.

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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