Background: Diabetes is a global health challenge that cusses high incidence of major social and economic consequences. As such, early prevention or identification of those people at risk is crucial for reducing the problems caused by it. The aim of study was to extract the rules for diabetes diagnosing using genetic programming. Methods: This study utilized the PIMA dataset of the university of California, Irvine. This dataset consists of the information of 768 Pima heritage women, including 500 healthy persons and 268 persons with diabetes. Regarding the missing values and outliers in this dataset, the K-nearest neighbor and k-means methods are applied respectively. Moreover, a genetic programming model (GP) was conducted to diagnose diabetes as well as to determine the most important factors affecting it. Accuracy, sensitivity and specificity of the proposed model on the PIMA dataset were obtained as 79. 32, 58. 96 and 90. 74%, respectively. Results: The experimental results of our model on PIMA revealed that age, PG concentration, BMI, Tri Fold thick and Serum Ins were effective in diabetes mellitus and increased risk of diabetes. In addition, the good performance of the model coupled with the simplicity and comprehensiveness of the extracted rules is also shown by the experimental results. Conclusions: GPs can effectively implement the rules for diagnosing diabetes. Both BMI and PG concentration are also the most important factors to increase the risk of suffering from diabetes.

The role and importance of rainfall-runoff process in water resources studies has led this process to be considered by many researchers. Different methods such as artificial neural networks, fuzzy systems, neurofuzzy, wavelet analysis, genetic algorithm, genetic programming and stochastic differential equations have been developed for rainfall-runoff modeling. Furthermore, genetic programming which involves a mathematical model relating output and input variables, is able to select input variables that effectively contribute to the model. In this research, genetic programming (GP) was applied to modeling of daily basis rainfall-runoff process in Lighvan watershed with area of 76.19 km2.According to the ability of GP in selecting the best variables, the significant variables were selected after 10 times running of GP. Modeling process was carried out using selected variables as well as two sets of mathematical operators. Comparing the results obtained for both models indicated that correlation coefficients and mean square errors using training data set were equal for both of them i.e.0.85 and 0.06, respectively. For the test data the coefficients became 0.93, 0.2 for set (1) and 0.97 and 0.08 for set (2), respectively. The model obtained from set (2) of the mathematical operators, was selected as the desirable one for the rainfall-runoff analysis in the watershed.

In this study we provide insurance companies with a tool to classify the risk level and predict the possibility of future claims. The support vector machine (SVM) and genetic programming (GP) are two approaches used for the analysis. Basically, in Iran insurance industry there is no systematic strategy to evaluate the car body insurance policy. Companies refer mainly to the world experience and employ it to rate the premium. An insurance claim dataset provided by an Iranian insurance company with a sample size of 37904 is considered for programming and analysis. According to the structure of the dataset, a supervised learning algorithm was used to describe the underlying relationships between variables. The model accuracy is over 90% and the outcomes indicate that car type, car plate, car color and car age were the main four factors contributing in prediction of claims.

Peer-to-Peer systems have been the center of attention in recent years due to their advantage. Since each node in such networks can act both as a service provider and as a client, they are subject to different attacks. Therefore it is vital to manage confidence for these vulnerable environments in order to eliminate unsafe peers. This paper investigates the use of genetic programing for achieving trust of a peer without central monitoring. A model of confidence management is proposed here in which every peer ranks other peers according to calculated local confidence based on recommendations and previous interactions. The results show that this model identifies malicious nodes without the use of a central supervisor or overall confidence value and thus the system functions.

Background: The forecasting of daily outpatient visits has significant practical implications in outpatient clinic operation management, not only contributing to guiding long-term resource planning and scheduling but also making tactical resolutions for short-term adjustments on special days such as holidays. We here in propose an effective genetic programming (GP)-based forecasting model to predict daily outpatient visits (OV) in a primary hospital. Methods: In the GP-based model, the holiday-based distance outlier mining algorithm was used to determine the holiday effect. In addition, solar terms were applied as the smallest unit to more accurately determine the impact of a change in the climate on the outpatient volume. A segmental learning strategy also was used to predict the daily outpatient volume for the time series data. Results: The GP-based prediction could more effectively extract depth information from a finite training sample size and achieve a better performance for predicting daily outpatient visits, with lower root mean square error (RMSE) and higher coefficient of determination (R 2 ) values, than the seasonal autoregressive integrated moving average (SARIMA) model in the time range of holidays and the holiday effect. Conclusion: GP-based model can achieve better prediction performance by overcoming the shortcomings of the SARIMA model. The results can be applied to support decision-making and planning of outpatient clinic resources, to help managers implement periodic scheduling of available resources on the basis of periodic features, and to perform proactive scheduling of additional resources.

Two-way slabs are one of the common structural systems. The benefits of such systems have led to extensive use of them in building construction. However, these systems are prone to pushing shear problem which causes sudden failure. There are lots of equations to predict punching shear of slabs. The main proportion of the existing equations are based on statistical results from previous experimental studies. However, these equations are approximate and have large errors. Therefore, more exact and reliable equations that can estimate punching shear capacity are desirable. The aim of this study is to propose an applicable method to predict punching shear in thin and thick slabs using artificial intelligence. For this reason Genetic Programming (GP) and Biogeography-Based Programming (BBP) are employed to find a relationship between punching shear and the corresponding effective parameters. GP that is inspired by natural genetic process, searches for an optimum population among the various probable ones. Two main operations of GP are crossover and mutation which make it possible to form new generations with better finesses. Unlike the GP, BBP is a Biogeography-Based Optimization (BBO) technique which is inspired by the geographical distribution in an ecosystem. BBP employs principles of biogeography to create computer programs. First, 267 experimental data is collected from the past studies. Next, using the aforementioned algorithms, a relationship to predict punching shear is proposed. To evaluate the error of prediction, several error functions including RMSE, MAE, MAPE, R, and OBJ are utilized. Matlab software is used to build the models of prediction. 10 different models are built and the one with the minimum error is selected. Based on the results, GP3 and BBP9 models could reach the best fitness. These models contain 3 sub-trees that use operators of plus, minus, multiplication, division, ln, sin, power 2, power 5 power 0. 5, power 0. 33, power 0. 2, and power 0. 25. Overall, the final tree includes several variables and integers, the variables are inputs of column dimension, effective depth, rebar ratio, compressive strength of concrete, and yielding strength of the rebars, and the output of punching shear capacity. The results of modeling are compared with recommended values of the ACI318 and EC2 codes. Comparison shows that code equations are scattered and therefore are not very reliable. Maximum error for both model and code equations occurs when the yielding strength of the rebars is low. Minimum estimation is related to GP and ACI codes with the ratio of 0. 485 and 0. 52, respectively which is due to very low thickness of the slab (41 to 55 mm). The maximum estimated shear belongs to ACI code in which the estimated value is two times the real one. Also, standard deviation of ACI values is about two times the others. Among the code equations, EC2 values yield more accurate results. However, GP and BBP models give much less mean error. Also, standard deviation of these methods is less than code values. In total, results show that the methods based on artificial intelligence are able to estimate pushing shear with around 2% error, compared to existing code equations which give 14-28% error.