genetic algorithm

In this research, predicting the logP of 34 types different pyrethroid derivatives was studied using quantitative structure-property relationship. The logP of studied pyrethroids was modeled using the genetic algorithm based on linear regression method (GA-MLR). It was found that the three effective descriptors GATS4P, PW3 and ZM1V have a reasonable correlation with logP, and led to the creation of a model with the highest regression coefficient and the lower error. The evaluation of GA-MLR model predictive ability for test set was performed by statistical parameters such as R2= 0.862, R2adj = 0.848, F=62.296 and MSE = 0.503. Also, the value of Q2LOO= 0.861 using the cross-validation method, and the values of R2 =0.880 and 0.929 for the training and test sets respectively, in the external validation method showed a very good correlation between experimental and prediction values. It was specified that the MLR model was reliable for predicting the logP of pyrethroid insecticides, and had sufficient accuracy with the lowest error.

Regarding the importance of greenhouse productions and high activity of pests including Trialeurodes vaporariorum under greenhouse conditions, the management of this pest requires ecological studies with a new approach. Therefore, due to the influence of the host plant characteristics on biological performance of greenhouse whitefly, the current research was performed to predict and estimate the values of its population growth parameters including net reproduction rate (R0) and intrinsic rate of population increase (rm). Estimation was based on some morphological features of the host plants using a MLP artificial neural network. The network was optimized with a genetic algorithm. The R0 and rm values of T. vaporariurum were calculated on two host plants, Cucumis sativus L. and Cucumis metuliferus May. Moreover, density and length of the leaf trichomes, density and area of leaf stomata cell of the lower leaf surface and the amount of leaf chlorophyll of each host plant was measured. The MLP neural network with optimal algorithm was designed. In order to evaluate the MLP neural network the T-test, F-test and Kolmogorov-Smirnov test were used to compare mean, variance, and statistical distribution, respectively. The obtained coefficient of determination (R2 = 0.9621) and probability level (P > 0.773) of statistical tests indicated high accuracy and capability and high generalizability of the MLP neural network for estimating R0 and rm of greenhouse whitefly.

Single strand DNA or RNA known as aptamers, are able to detect the target molecule with specificity at the monoclonal antibody level. However, the placement of mycotoxins in small molecule groups and their high molecular weight differences with aptamers have become a serious challenge in introducing aptameric probes for them. In the present study, in order to achieve a new and shorter aptamers sequence for aflatoxin B1 (AFB1), an initial oligonucleotide library (Lib1) was designed based on the sequence of a known AFB1 aptamer (named Apt1, 50 bp) using the genetic algorithm. The best aptamer from the Lib1 library was selected based on the molecular docking results and has been modified to create a new library (Lib2) using the truncating strategy. Virtual screening of the Lib2 library in terms of their binding affinity over AFB1 molecule led to obtain the truncated aptamer, C52-T, with 19 bp in length. Type of interaction and stability of C52-T-AFB1 complex were investigated using molecular dynamics simulations (MD) and MM-PBSA method. The affinity constant of C52, C52-T and Apt1 aptamers over AFB1 were estimated through unmodified gold nanoparticle-based colorimetric assay. The experimentally founding and in silico results were completely consistent. It seems that the computational techniques have the great potential to introduce sensitive molecular probes to design specific mycotoxins aptasensors.

The common pistachio psylla, Agonoscena pistaciae (Hemiptera: Aph alaridae), is a key pest of pistachio trees. Nymphs and adults suck sap from leaves resulting in defoliating, falling flower buds, stopping tree growth and finally yield loss. The population dynamics of insects is influenced by several physical and biological factors such as temperature and natural enemies. Identifying the key factor in population dynamics is difficult due to the potential interactions between biological and environmental factors. Non-linear analysis methods such as artificial neural networks (ANNs) are suited to be applied in the ecosystem with non-linear and complex ecological data. These methods have been widely used as a robust information-processing instrument in many research fields, especially in predicting pest occurrence. For example, a neural model is used to predict bionomic variables related to the nutritional dynamics of blowflies.
In the present investigation, the seasonal abundance of A. pistaciae in a pistachio orchard was evaluated for two years. This study aimed to assess the performance of ANN in representing nonlinear dynamic data for common pistachio psyllid populations. To this end, back propagation ANN was implemented to evaluate the relationship between the pest occurrence and influential factors.
 

The population density of ‎psyllids was monitored weekly by the yellow sticky trap for the adult and direct counting for ‎the nymph. After collecting the related data, the curves of the seasonal dynamic population of adults and nymphs were drawn. Then, the number of generations and duration activity of psyllid in each generation were determined. Multi-layer perceptron neural network ‎‎(MLP), genetic algorithm (GA) and multi-linear regression (MLR) were used to determine the relative significance of biotic (natural enemies) and ‎abiotic (weather variables) factors for predicting A. pistaciae density. An ANN model was designed by using the inputs (average temperature, average rainfall, average relative humidity, wind speed and direction,  and population of natural enemies), hidden layer (the number of neurons in the hidden layers determined by trial and error), and one neuron in the output layer (the occurrence amount for predicting the population). The Levenberg–Marquardt algorithm was used as the learning algorithm. The root mean square error (RMSE) and coefficient of determination (R2) were statistics, calculated for both the training and testing set for each iteration.

The population fluctuations of A. pistaciae on Akbari pistachio cultivar during 2015 and 2016 indicated that the psyllid populations in the field had five apparent peaks from late March to October. Agonoscena pistaciae in Rafsanjan county had six complete and one incomplete generation in 2007 and 2008 (9). The general population trends were similar over time within two years, but population densities of adults and nymphs were higher in 2016. Statistical comparison of weather variables between two years showed no significant difference. 
Several topologies were examined and the best result was obtained with 15 and 9 neurons in the first and second hidden layer for both adult and nymph in MLP method, respectively. In genetic algorithm, a hidden layer with 14 neurons for adult and 16 neurons for nymph was employed. The R2 values of MLR, MLP and GA methods (at test phase) were 0.32, 0.61, 0.73, respectively and the RMSE values were 31.79, 0.223 and 0.083, respectively for adult. In the prediction of the population density of the nymph by MLR, MLP and GA, the R2 values were obtained to be 0.22, 0.84, 0.88, respectively, and the RMSE values were 48.03, 0.051 and 0.051, respectively.

The R2 and RMSE values showed reliable performance of ANN and GA. The ANNs also modeled the numbers of the psyllid with high accuracy. In addition, the higher R2 and lower RMSE were obtained for MLP and GA methods relative to MLR. It has been reported in the related literature that the ANN consistently outperformed the statistical models. The ANN as a nonlinear predictor exhibited a high accuracy in predicting the richness of aquatic insect species in running waters by a set of four environmental variables (21). Based on the principal components analysis and back propagation artificial neural methods to analyze historical data on the population occurrence of Scirpophaga incertulas, the new model could improve the prediction accuracy, compared with other methods (27). It is worth noting that in regression models, the weak correlation between dependent and independent variables does always not imply that these variables are not associated, as they may have a nonlinear correlation.

The Mediterranean pine engraver, Orthotomicus erosus (Wollaston) (Coleoptera: Curculionidae: Scolytinae), is one of the most important pests of pine trees in Kermanshah. Attack and feeding of this pest destroy the floem tissues under the bark of the infected trees and disrupt the plant sap flow, causing the death of infected trees. The purpose of this study was to predict and mapping the distribution of O. erosus using multi-layer perceptron neural networks combined with genetic and imperialist competitive algorithms in Kermanshah. The sampling of pine trees was done in 2015-2016 in Kermanshah. To evaluate the ability of the used neural networks to predict the distribution was used statistical comparison the parameters such as mean, variance and statistical distribution between actual and predicted values by multi-layer perceptron neural networks combined with genetic and imperialist competitive algorithms. Results showed that in training and test phases, was no significant differences between average, variance and statistical distribution of actual and predicted data that indicates the high accuracy and the ability of neural networks to map the distribution of this pest in Kermanshah. The R2 values revealed that imperialist competitive algorithm had a higher accuracy to estimate the density of O. erosus compared with the other two methods. In addition, the comparison of the coefficients of the R2 between different neural networks and geostatistics method showed that all three neural network models predicted the distribution pattern of O. erosus better than the geostatistics method. The maps drawn by all three neural networks showed that the distribution of this pest was cumulative. The results obtained from the geostatistics method represented the cumulative distribution of the pest.

Today, with the advance statistical techniques and neural networks, predictive models of distribution were rapidly developed in ecology. Due to the difficulty of sampling, there are usually not enough samples in such studies. Therefore, in order to predict and mapping the distribution of Coccinella
septempunctata used the combination of the Kriging method with multilevel perceptron neural networks (MLP) combined with genetic algorithm at the farm level. Population data of pest was obtained in 2014 by sampling in 221 fixed points in the alfalfa farm of Bajgah. The data was interpolated by ordinary Kriging method with spherical variogram, which had the best performance, and introduced as a neural network input. To evaluate the ability combined geostatistics with optimized artificial neural network by genetic to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and between predicted values and actual values. Results indicating that there was non-significant difference between statistical parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated Coccinella septempunctata density. Our map showed that pest distribution was patchy.

In this study, Neuro Fuzzy network was used to estimate the spatial distribution of Bemisia tabaci in a cucumber field in Behbahan. Pest density assessments were performed based on a 10 m × 10 m grid pattern pattern and a total of 100 sampling units in. In this method latitude and longitude information was used the input data and output of method showed the number of pest. To determine the sensitivity of this method to different levels of the pest after collecting samples, automatic clustering method was used to determine the number of clusters Davies and Bouldin index was used to evaluae criterion. In order to finding the answer, Clustering Search Space Genetic Algorithm was used.Davies and Bouldin index (0.46) showed that the data should be divided into three clusters. Results indicated average, variance, statistical distribution and also coefficient of determination in the observed and the estimated Bemisia tabaci density were not significantly different.Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field.