• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.577-591
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• 2022

This paper develops a comparatively time-efficient methodology for performing seismic fragility analysis of the reinforced concrete (RC) buildings in the presence of uncertainty sources. It aims to appraise the effectiveness of any variation in the material's mechanical properties as epistemic uncertainty, and the record-to-record variation as aleatory uncertainty in structural response. In this respect, the fuzzy set theory, a well-known 𝛼-cut approach, and the Genetic Algorithm (GA) assess the median of collapse fragility curves as a fuzzy response. GA is requisite for searching the maxima and minima of the objective function (median fragility herein) in each membership degree, 𝛼. As this is a complicated and time-consuming process, the authors propose utilizing the Gene Expression Programming-based (GEP-based) equation for reducing the computational analysis time of the case study building significantly. The results indicate that the proposed structural analysis algorithm on the derived GEP model is able to compute the fuzzy median fragility about 33.3% faster, with errors less than 1%.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.999-1009
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• 2004

As a new approach to the diagnosis of cancers, bioinformatics attracts great interest these days. Machine teaming techniques have produced valuable results, but the field of medicine requires not only highly accurate classifiers but also the effective analysis and interpretation of them. Since gene expression data in bioinformatics consist of tens of thousands of features, it is nearly impossible to represent their relations directly. In this paper, we propose a method composed of a feature selection method and genetic programming. Rank-based feature selection is adopted to select useful features and genetic programming based arithmetic operators is used to generate classification rules with features selected. Experimental results on Lymphoma cancer dataset, in which the proposed method obtained 96.6% test accuracy as well as useful classification rules, have shown the validity of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.1-8
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• 1998

This paper proposes a reinforcement genetic programming based on the reinforcement learning method for the performance improvement of genetic programming. Genetic programming which has tree structure program has much flexibility of problem expression because it has no limitation in the size of chromosome compared to the other evolutionary algorithms. But worse results on the point of convergence associated with mutation and crossover operations are often due to this characteristic. Therefore the sizes of population and maximum generation are typically larger than those of the other evolutionary algorithms. This paper proposes a new method that executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. The validity of the proposed method is evaluated by appling it to the artificial ant problem.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1229-1237
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• 2005

Combining multiple classifiers has been actively exploited to improve classification performance. It is required to construct a pool of accurate and diverse base classifier for obtaining a good ensemble classifier. Conventionally ensemble learning techniques such as bagging and boosting have been used and the diversify of base classifiers for the training set has been estimated, but there are some limitations in classifying gene expression profiles since only a few training samples are available. This paper proposes an ensemble technique that analyzes the diversity of classification rules obtained by genetic programming. Genetic programming generates interpretable rules, and a sample is classified by combining the most diverse set of rules. We have applied the proposed method to cancer classification with gene expression profiles. Experiments on lymphoma cancer dataset, prostate cancer dataset and ovarian cancer dataset have illustrated the usefulness of the proposed method. h higher classification accuracy has been obtained with the proposed method than without considering diversity. It has been also confirmed that the diversity increases classification performance.

• Computers and Concrete
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• v.30 no.6
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• pp.377-391
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• 2022

The use of carbon fiber-reinforced polymers (CFRP) has widely increased due to its enhancement in the ultimate strength and ductility of the reinforced concrete (RC) structures. This study presents a prediction model for the axial compressive strength and strain of normal-strength concrete cylinders confined with CFRP. Besides, soft computing approaches have been extensively used to model in many areas of civil engineering applications. Therefore, the genetic expression programming (GEP) models to predict axial compressive strength and strain of CFRP-confined concrete specimens were used in this study. For this purpose, the parameters of 283 CFRP-confined concrete specimens collected from 38 experimental studies in the literature were taken into account as input variables to predict GEP based models. Then, the results of GEP models were statistically compared with those of models proposed by various researchers. The values of R² for strength and strain of CFRP-confined concrete were obtained as 0.897 and 0.713, respectively. The results of the comparison reveal that the proposed GEP-based models for CFRP-confined concrete have the best efficiency among the existing models and provide the best performance.

• Computers and Concrete
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• v.18 no.2
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• pp.155-163
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• 2016

This paper, proposes 20 models for predicting compressive strength of recycled aggregate concrete (RAC) containing silica fume by using gene expression programming (GEP). To construct the models, experimental data of 228 specimens produced from 61 different mixtures were collected from the literature. 80% of data sets were used in the training phase and the remained 20% in testing phase. Input variables were arranged in a format of seven input parameters including age of the specimen, cement content, water content, natural aggregates content, recycled aggregates content, silica fume content and amount of superplasticizer. The training and testing showed the models have good conformity with experimental results for predicting the compressive strength of recycled aggregate concrete containing silica fume.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.43-48
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• 2014

Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.

• Structural Engineering and Mechanics
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• v.36 no.6
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• pp.759-783
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• 2010

In this paper, compressive strength of carbon fiber reinforced polymer (CFRP) confined concrete cylinders is formulated using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA, and a robust variant of GP, namely multi expression programming (MEP). Straightforward GP/SA and MEP-based prediction equations are derived for the compressive strength of CFRP-wrapped concrete cylinders. The models are constructed using two sets of predictor variables. The first set comprises diameter of concrete cylinder, unconfined concrete strength, tensile strength of CFRP laminate, and total thickness of CFRP layer. The most widely used parameters of unconfined concrete strength and ultimate confinement pressure are included in the second set. The models are developed based on the experimental results obtained from the literature. To verify the applicability of the proposed models, they are employed to estimate the compressive strength of parts of test results that were not included in the modeling process. A sensitivity analysis is carried out to determine the contributions of the parameters affecting the compressive strength. For more verification, a parametric study is carried out and the trends of the results are confirmed via some previous studies. The GP/SA and MEP models are able to predict the ultimate compressive strength with an acceptable level of accuracy. The proposed models perform superior than several CFRP confinement models found in the literature. The derived models are particularly valuable for pre-design purposes.

• Earthquakes and Structures
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• v.15 no.4
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• pp.403-410
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• 2018

The choice of attenuation relationships is one of the most important parts of seismic hazard analysis as using a different attenuation relationship will cause significant differences in the final result, particularly in near distances. This problem is responsible for huge sensibilities of attenuation relationships which are used in seismic hazard analysis. For achieving this goal, attenuation relationships require a good compatibility with the target region. Many researchers have put substantial efforts in their studies of strong ground motion predictions, and each of them had an influence on the progress of attenuation relationships. In this study, two attenuation relationships are presented using seismic data of Mazandaran province in the north of Iran by Genetic Expression Programming (GEP) algorithm. Two site classifications of soil and rock were considered regarding the shear wave velocity of top 30 meters of site. The quantity of primary data was 93 records; 63 of them were recorded on rock and 30 of them recorded on soil. Due to the shortage of records, a regression technique had been used for increasing them. Through using this technique, 693 data had been created; 178 data for soil and 515 data for rock conditions. The Results of this study show the observed PGA values in the region have high correlation coefficients with the predicted values and can be used in seismic hazard analysis studies in the region.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.223-228
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• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.