Automatically Defined Functions in Gene Expression Programming

Ferreira, Cândida

doi:10.1007/11521433_2

Cited by 32 publications

(51 citation statements)

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“…GEP approach has a few advantages: (a) simplicity of chromosomes for manipulation (b) The expression tree of GEP is exclusive for related chromosomes (c) gives explicit formulation for predicting the parameter (d) there is no constraints for the complexity of chromosomes structures [23]. The main difference between GA and GEP is in the existence of individuals where in GA, individuals have linear threads and static lengths which are called chromosomes, but in GPA the threads can be nonlinear with variable lengths.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

Mehdipour

Memarianfard

2017

CivileJournal

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Air pollution became fatal issue for humanity and all environment and developed countries unanimously allocated vast investments on monitoring and researches about air pollutants. Soft computing as a novel way for pollutants prediction can be used for measurement tools calibration which can coincidently decrease the expenditures and enhance their ability to adapt quickly. In this paper support vector machine (SVM) and gene expression programming (GEP) as two powerful approaches with reliable results in previous studies, used to predict tropospheric ozone in Tehran metropolitan by using the photochemical precursors and meteorological parameters as predictors. In a comparison between the two approaches, the best model of SVM gave superior results as it depicted the RMSE= 0.0774 and R= 0.8459 while these results of gene expression programming, respectively, are 0.0883 and 0.7938. Sensitivity of O3 against photochemical precursors and meteorological parameters and also for every input parameter, has been analysed discreetly and the gained results imply that PM2.5, PM10, temperature, CO and NO2 are the most effective parameters for O3 values tolerances. For SVM, several kernel tricks used and the best appropriate kernel selected due to its result. Nonetheless, gamma and sin2 values varied for every kernel and in the last radial basis function kernel opted as the best trick in this study. Finally, the best model of both applications revealed, and the resulted models evaluated as reliable and acceptable.

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Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

Mehdipour

Memarianfard

2017

CivileJournal

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“…Thus, a formal definition of the symbolic regression problem is presented here to aid readers in comprehending our proposed methodology better. Then the traditional gene expression approach of GEP [5] is presented, followed by a description of the extended gene expression approach that incorporates ADFs as introduced in GEP-ADF [23]. Finally, other related GEP variants are discussed.…”

Section: Preliminariesmentioning

confidence: 99%

“…In [23], Ferreira combined ADF with GEP to arrive at the GEP-ADF. In GEP-ADF, each chromosome consists of a number of conventional genes and a homeotic gene, as illustrated in Fig.…”

Section: Extended Gene Expression Technique Used In Gep-adfmentioning

confidence: 99%

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Self-Learning Gene Expression Programming

Zhong

Ong

Cai

2016

IEEE Trans. Evol. Computat.

108

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In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In contrast to the existing GEP variants, the proposed SL-GEP features a novel chromosome representation where each chromosome is embedded with subfunctions that can be deployed to construct the final solution. As part of the chromosome, the subfunctions are self-learned or self-evolved by the proposed algorithm during the evolutionary search. By encompassing subfunctions or any partial solution as input arguments of another subfunction, the proposed SL-GEP facilitates the formation of sophisticated, higher-order, and constructive subfunctions that improve the accuracy and efficiency of the search. Further, a novel search mechanism based on differential evolution is proposed for the evolution of chromosomes in the SL-GEP. The proposed SL-GEP is simple, generic and has much fewer control parameters than the traditional GEP variants. The proposed SL-GEP is validated on 15 symbolic regression problems and six even parity problems. Experimental results show that the proposed SL-GEP offers enhanced performances over several state-of-the-art algorithms in terms of accuracy and search efficiency.

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“…4). One feature of GEP is that the set of genetic operators applied to the chromosomes always produces valid expression [19].…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach

Barton

2010

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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Abstract-Proteomics involves collecting and analyzing information about proteins within one or more complex samples in order to address a biological problem. One methodology is the use of high performance liquid chromatography coupled mass spectrometry (nanoLC-MS). In such a case, the accurate determination of non-linear peptide retention times between runs is expected to increase the number of identified peptides and hence, proteins. There are many approaches when using a computer for such a problem; including very interactive to completely non-interactive algorithms for finding global and local functions that may be either explicit or implicit.This

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Automatically Defined Functions in Gene Expression Programming

Cited by 32 publications

References 0 publications

Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

Self-Learning Gene Expression Programming

Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach

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