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Inference of gene expression networks using memetic gene expression programming

- Zarnegar, Armita, Vamplew, Peter, Stranieri, Andrew

  • Authors: Zarnegar, Armita , Vamplew, Peter , Stranieri, Andrew
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at Thirty-Second Australasian Computer Science Conference (ACSC 2009), Wellington, New Zealand : Vol. 91, p. 17-23
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  • Description: In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression networks are modelled by differential equations which represent temporal gene expression relations. Gene Expression Programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a memetic algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies.

A new model based on gene expression programming to estimate air flow in a single rock joint

- Khandelwal, Manoj, Armaghani, Danial, Faradonbeh, Roohollah, Ranjith, Pathegama, Ghoraba, Saber

  • Authors: Khandelwal, Manoj , Armaghani, Danial , Faradonbeh, Roohollah , Ranjith, Pathegama , Ghoraba, Saber
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Environmental Earth Sciences Vol. 75, no. 9 (2016), p.
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  • Description: This paper is aimed to introduce and validate a gene expression programming (GEP) model to estimate the rate of air flow in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure and air outlet pressure. To achieve the aim of this study, a series of laboratory experiments were designed and carried out and then a database comprising 47 datasets was prepared to develop new predictive models. A gene expression programming (GEP) model for prediction of air flow was proposed using the prepared datasets. In this regard, a series of sensitivity analyses were performed to choose the best GEP model. For comparison purposes, multiple regression (MR) analysis was also employed for air flow estimation. Several performance indices, i.e., coefficient of determination (CoD), mean absolute error (MAE), root mean square error (RMSE) and variance account for (VAF) were considered and calculated to evaluate the performance prediction of the developed models. Considering both training and testing datasets, the developed GEP model can provide higher performance prediction of rate of air flow in comparison to the MR model. © 2016, Springer-Verlag Berlin Heidelberg.
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