Designing Neural Networks Using Gene Expression Programming

Ferreira, Cândida

doi:10.1007/3-540-31662-0_40

Cited by 49 publications

(39 citation statements)

References 13 publications

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“…For the first time, Ferreira evolved the gene expression programming as a developed method with the base of genetic algorithms (GA) and has been used extensively in the latest studies [22]. 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].…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

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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%

“…Figure 1. [22] shows the GEP process. The procedure to make a model for O3 prediction (as dependent) using 8 different input combinations (as independents), is: 1-opting a fitness function 2-create chromosomes 3-chromosome structure 4-linking functions 5-genetic functions [26].…”

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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“…On the other hand, in case of GEP in late evolution, when the algorithm approaches convergence, owing to the difference of individual fitness in small population, it is unlikely to continue to evolve. And if the fitness function design is poor, the algorithm will be stagnant [6]. In order to solve this contradiction, this study uses the formula 1 as the fitness function of the algorithm.…”

Section: Fitness Function Designmentioning

confidence: 99%

“…Bhattacharyya [6] distance is used to measure the classification information of genes; B is Bhattacharyya distance of gene.…”

Section: Use Signal To Noise Ratio To Reject Independent Genementioning

confidence: 99%

Empirical Analysis of Genetic Evolution Algorithm Based on Multi-layer Chromosome of Gene Expression Programming and K-means Clustering Algorithm

Zhang¹,

Guo²

2014

TOAUTOCJ

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Abstract:As hypertension has become one of the principal diseases affecting human health, and its prediction accuracy is a topic of concern for the medical professionals, so the computer-aided prediction model of target organ damage of primary hypertension is worthy of research. GEP model with simple chromosome, linear, compact, easily genetic manipulation can eliminate the correlation of gene expression inputs in a variety of training samples. And it has the intelligent, more flexible model structure, having wider methodological applicability, higher prediction accuracy and other characteristics. This study shows that the prediction model has a great prospect for application in the auxiliary prediction of target organ damage in primary hypertension. And classification accuracy is 87.5%, which is higher than SVM and BP neural networks.

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“…The selection operator is realized in DEA by combining with statistical mechanics. Gene Expression Programming (GEP) is presented firstly by Candida Ferreira [11], [12], [13]. It is a useful modeling method which is especially effective for function finding.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Evolution in Function Finding

Jiang

Wei

et al. 2007

Third International Conference on Natural Computation (ICNC 2007) Vol V

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As a branch of evolutionary computation (EC), genetic programming (GP) and genetic expression programming (GEP) have been applied widely in auto-programming. Inspired by the statistical mechanism, a new selection operator based on statistical mechanics is proposed, an algorithm-dynamical evolutionary programming (DEP) algorithm is constructed by applying this new selection operator. The experimental results show that DEP is better than GEP in terms of the quality of average fitness.

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Designing Neural Networks Using Gene Expression Programming

Cited by 49 publications

References 13 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

Empirical Analysis of Genetic Evolution Algorithm Based on Multi-layer Chromosome of Gene Expression Programming and K-means Clustering Algorithm

Dynamical Evolution in Function Finding

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