genetic algorithms

Lipid oxidation is important issues that can lead to the degradation and destruction of foods containing lipids. A number of antioxidants have been used to solve this problem. Stachys lavandulifolia is a medicinal herb with antioxidant properties. Given that the impact of new technologies compared to traditional methods in terms of saving time, energy, and increase the efficiency of extraction have been identified. The aim of this study was modeling the extraction of antioxidant compounds from Stachys lavandulifolia by ultrasound-assisted extraction method. For this purpose, to model the extraction efficiency of neural network antioxidant compounds, artificial neural network hybrids - genetic algorithm and response surface methodology were used. The best model was obtained based on the results of the neural network model with gradient optimization method, with trainbr training and tansig transfer function and the number of hidden layers of this combination with two neurons 8 in the first layer and 4 in the second layer. For this network structure, an error of 0.0128 and a correlation coefficient of 97.30% were determined. By comparing this method with the response level, the model accuracy increased from 92% to 94.68%. The best result for the hybrid model occurred in the trainbr learning algorithm with the tansig transfer function with a hidden layer and 18 neurons. The error rate and correlation coefficient in this method were equal to 0.0693 and 83.27%, respectively. According to the results of the neural network with the logger method, it answered better and the hybrid method of the genetic algorithm with the neural network was not a suitable model for prediction. Finally, it can be said that mountain tea can be considered as a potential source of antioxidant compounds and neural network can be considered as a successful application method to predict the extraction efficiency of antioxidant compounds.

Soil hydraulic properties have key role in sugar cane cultivation management. The purpose of this study is to estimate soil moisture characteristic curve using an artificial neural network and its optimization with genetic algorithm. Therefore, based on the cultivation operations management and soil properties included: organic matter content, soil texture, electrical conductivity, sodium adsorption ratio, 4 land unit tracts in Debel-Khozaii, Amir-Kabir, Karoon and Haft-Tapeh agro-industries were selected. A total of 310 soil samples from both 0-40 and 40-80 cm of soil profiles were collected. In this study, five models were arranged in hierarchy to estimate soil hydraulic properties with ANNs. The performances of the models were evaluated using Spearman's correlation coefficient (r) between the observed and the estimated values, normalized mean square error (NMSE), and mean absolute error (MAE). Owing to the fact that the selection of each of the variable parameters of neural network necessitated recurring trails and errors, and consequently teaching a large number of networks with various topologies, genetic algorithm method was utilized for finding the optimization of these parameters and the efficiency of this method was examined in terms of the optimization of neural network. Results showed that the neural network has a high degree of accuracy in modeling and estimating soil moisture characteristic curve (R =0.943, MAE=0.019, NMSE=0.054). Also, combining artificial neural networks with genetic algorithm for optimizing the conditions of the artificial neural networks implementation was positive and combining approach indicated its superiority over non-optimized implementation of artificial neural networks in all cases (R=0.985, MAE=0.01, NMSE =0.0151).

One of the most important issues in the management of production is how to select the best option for each production activity, so that the time and cost of production are minimized and the quality is simultaneously maximized. Considering a large number of activities and possible options for them, this choice does not have a unique solution and can be used the utility function and assign weights to time, cost and quality in order to select the best answer from those obtained. Since in reality a vast amount of uncertainty can be observed, an effective management necessitates dealing with these uncertainty issues. In this study, a fuzzy math model for a chain of activities is suggested to determine the best practical method employed by the milk production process. In order to achieve this goal, fuzzy alpha cut was employed to manage the risk of these series of activities. Accordingly, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with fuzzy inputs was used and the best method of implementation for each activity of milk production process from the starting point of cows birth to the ending point of removing these cows from the herd was introduced.

Soil hydraulic properties have key role in sugar cane cultivation management. The purpose of this study is to estimate soil moisture characteristic curve using an artificial neural network and its optimization with genetic algorithm. Therefore, based on the cultivation operations management and soil properties included: organic matter content, soil texture, electrical conductivity, sodium adsorption ratio, 4 land unit tracts in Debel-Khozaii, Amir-Kabir, Karoon and Haft-Tapeh agro-industries were selected. A total of 310 soil samples from both 0-40 and 40-80 cm of soil profiles were collected. In this study, five models were arranged in hierarchy to estimate soil hydraulic properties with ANNs. The performances of the models were evaluated using Spearman's correlation coefficient (r) between the observed and the estimated values, normalized mean square error (NMSE), and mean absolute error (MAE). Owing to the fact that the selection of each of the variable parameters of neural network necessitated recurring trails and errors, and consequently teaching a large number of networks with various topologies, genetic algorithm method was utilized for finding the optimization of these parameters and the efficiency of this method was examined in terms of the optimization of neural network. Results showed that the neural network has a high degree of accuracy in modeling and estimating soil moisture characteristic curve (R =0.943, MAE=0.019, NMSE=0.054). Also, combining artificial neural networks with genetic algorithm for optimizing the conditions of the artificial neural networks implementation was positive and combining approach indicated its superiority over non-optimized implementation of artificial neural networks in all cases (R=0.985, MAE=0.01, NMSE =0.0151).

One of the core elements of water management and increase agricultural productivity is the efficient use of available resources. Due to increasing demand for agricultural products, increase the productivity of scarce resources is essential. Optimal utilization of these resources, in addition to meet the demand of the community as a major goal, can also increase operating income to be followed. One of the solutions for increasing productivity in the agricultural sectoris reforming the pattern of cultivation according to different parameters. In this study, using economic criteria, water requirements and crop season in the TOPSIS multi-criteria decision, efficient products were selected. Furthermore, by combining these results with the parameters of the water and common time development of products using a genetic algorithm, the optimal cultivation area allocated to each product specified. In addition, by using GIS spatial capabilities and parameters of different locations and combined with optimized data, map the optimal model has been developed crops. The results show that using the models used and the capabilities of GIS, suitable cropping pattern can be achieved, and, on average, raised by about 25% efficiency and the use of available resources.

This study has two purposes. The first is evaluating the effect of technology spillover on the Iran’s agricultural exports through VECM method. The second goal is to investigate and compare the performance of GA against the VECM approach and identifying the best approach to estimate the pattern of agricultural exports. The findings indicate that the technology spillover have a significant positive effect on Iran’s agricultural exports. The effect of other variables; terms of trade, relative price index, the value added of agriculture sector and real exchange rate are significantly positive and only the effect of the trade partner’s GDP is negative statistically. Also a comparison of the GA and VECM performance shows that GA method, in estimating the pattern of Agriculture exports, has a greater optimum performance than VECM and is identified as the preferred method for optimizing agricultural export function. So, according to the positive effects of technology spillover on the agricultural exports, it is worthy, by creating a safe space for investing, infrastructural facilities and Prioritization of high yield sections, to provide the possibility of attracting more FDI and using the donations of technology spillover more efficient. Also, according to high performance of GA in optimization, utilization of this method is recommended in scientific studies.

In order to reduce sediment and soil conservation plan generation، the design and calculation of the exact size of the dam at dams، evaluation and estimation of sediment yield in the watershed of the dam، is essential. In general erosion and sediment transport of the most complex issues that determine the exact hydrodynamic equations for the various variables، it is not easy. The aim of this study was to simulate suspended sediment load using a neural-fuzzy model is combined This is necessary for the various input combinations using stepwise regression، genetic algorithm model and gamma test and then determine the most suitable model for the input. Karaj Dam Sierra station data in the period 1974-2011 (37 years) was used so that the 15 variables as input and output variables as was considered. Results indicated neuro-fuzzy models in a suitable combination of the number of training data to select the most appropriate modeling of the genetic algorithm to be achieved. It should be noted that in the case of all input variables، modeling neuro-fuzzy model was not appropriate Therefore، methods are necessary to determine the appropriate combination of inputs. Finally، the Genetic Algorithm model as the most appropriate method and stepwise regression، second، and third order gamma test as appropriate methods to determine combination of neuro-fuzzy model inputs selected. The neuro-fuzzy model when using a selected combination of genetic algorithms to simulate suspended sediment load 99/0 correlation coefficient and the mean square error as the most appropriate modeling 0000009/0 has been selected.

In this study، the demand for different types of meat including red meat، Poultry and fish have been analyzed from 1368 to 1390 in urban areas of Iran. In order to select the most Suitable model for demand، the SUR method out of Rotterdom،”Almost Ideal Demand System” (AIDS) and CBS models have been used. Results from comparison of these models demonstrated that، “Almost Ideal Demand System “(AIDS) is selected as the superior model for prediction in the study period. Then the prediction of major demand for meat groups in areas that mentioned above، Performed until 2025 (twenty-year vision of Islamic Republic of Iran). The results of this prediction، using genetic algorithm method and based on presupposition of continuation for previous incoming and pricing conditions، demonstrated that the share of the budget devoted to red meat، will gradually reduce for urban consumer and the share of the budget devoted to poultry and fish will increase. The demand rate of households from each these three groups for red meat، poultry and fish will increase. It means that the total demand rate for all kinds of meat will reduce as 2. 59 percent every year. And also red meat، poultry and fish are essential (normal) and Successor commodities for urban consumer. On the other hand، red meat and poultry are inelastic commodities and fish is an elastic commodity.

This study attempts to forecast the export of Iran''s date for the period of 2010-2016، using the econometric methods، ARMA، GARCH، and the methods of computational intelligence، artificial neural networks and genetic algorithms. The data from 1967-2009 were used. The data from 1967- 2005 were used for modeling and the four last years، were used to examine forecast ability. Results indicated that the neural network has the lowest Forecast error comparing other methods followed by Genetic Algorithm. According to the superiority of neural networks in forecasting the Iran''s date export، forecasts made by this، shows the decreasing-increasing trend in the exports of Iran''s date.

Population growth has caused increase of water demand for the drinking water, industry and agriculture. This condition needs the application of effective measures for optimal water management. So, in this research, a water allocation model is proposed for agriculture, industry and service sectors. In agricultural sector, production function of each crop is determined and then, objective function is specified based on the production function, and income of crops. In the industrial sector, the water demand of the product is function of water and other material prices and so, the demand function is determined based on these factors. Due to the necessity of water for the service sector, the total water demand of this section was fully allocated. Then, using innovative learning algorithms, a combination of genetic algorithms-Collective Intelligence (GAPSO), objective function is maximized and optimal allocation of water for agriculture and industry, were determined and compared. According to the result mentioned, use pattern of deficit irrigation model, changing crop pattern, remove the acreage of some crops and use of more water resources in the industry field can be effect on increase revenues to 114 billion Rls. In sum, the income of agriculture and industry in the Iran Central Kavir basin can be up to 56 percent of revenues of the current situation using water resource allocation for different sectors. In this case, the region will witness a remarkable progress. Therefore changes in the water resources allocation of the area seem to be necessary.

Based on the global presupposition, 52 countries will face water deficit crisis by 2025, therefore, it would be inevitable to use water resources efficiently and favorably. In this regard, fundamental and comprehensive changes in the current agricultural structure are vital, which is referred to as the largest water consuming activity, designing the cropping pattern in order to determine the cultivated area, proper rotation, and water use is of the great importance. In this study we aimed to present a model which could provide optimum cropping pattern, cultivation area, and allowable deficit irrigation during growing season, using genetic algorithms. The results showed that the model did not suggest deficit irrigation during the cultivation period, instead full irrigation was suggested. Furthermore, the result of sensitivity analysis indicated that by increasing the price of water, the model has made 60 percent difference in cropping pattern in such a way that the maximum cropping area belonged to fully irrigated Maize (3.5 ha) and by decreasing the amount of applied water, the model decreased the area under wheat cropping and increased the amount of deficit irrigation by 20.32 percent.