To estimate the genetic trend for lactation milk yield, a total of 38457 monthly test day records obtained from first calving of Holstein cows, recorded during 1995-2005 at Astan-e Quds-e Razavi Dairy Farm were used. A random regression test day animal model was applied to analyse the records. In the model, the environmental fixed effects of milking times, sperm origin and herd-year-month of recording were included. To take account of the shape of the lactation curve at genetic and permanent levels, orthogonal Legendre polynomials of order 4 were also fitted in the model. For progeny, genetic trend was estimated based upon regressing of mean predicted breeding value on year of zygote formation, birth and calving of animals. For sires and dams, the genetic trends were estimated based on regressing of mean predicted breeding value on the above corresponding years of their daughters. The genetic trend (based on the year of zygote formation) was 10.64 Kg per year (p<0.05) in the progeny. Non-significant genetic trends were observed for progeny based upon their year of birth and calving. For dams' population, significant genetic trends were found to be 8.77, 8.88 and 9.1 Kg per year based on year of zygote formation, birth and calving of their daughters respectively. The genetic trends in sires' population were all statistically non-significant (p>0.05).

In this study, data from 1882 ewes, including 3351 lambs born, was used as litter size trait in Afshari sheep breed. The data were collected from 2000 to 2016 by the research station at Zanjan University. The data were analyzed by ASReml software via threshold model in six different models. Fixed effects of year and season, age of ewe, number of parturition and genetic group [at five levels including pure Afshari, 50% Afshari (F1), 75% Afshari (B1), 87. 5% Afshari (B2) and 93. 75% Afshari (B3)] were significant (P<0. 05). Heritability and repeatability were estimated to be as 0. 08 and 0. 17, respectively. Genetic trends of this trait, in two periods between 2000 to 2006, before the gene introgression design as first period, and from 2007 to 2016, after the introgression as second period, were 0. 004 (P>0. 05) and 0. 059 (P<0. 05), respectively. Also, the phenotypic trends were 1. 37 (P>0. 05) and 1. 69 (P<0. 05) for two periods, respectively. The results showed that the FecB gene transfer increased the litter size. Consequently, because of the direct relationship between the more profitability of flock and the increase in the number of lambs born, the gene transfer design will be followed and more welcomed by sheep breeders. Therefore, genetic parameters and genetic trends estimation of litter size in this flock can be effective for developing continues genetic breeding strategy.

In this study, the genetic and phenotypic trend of milk and fat production traits were estimated form 94121 cows’ first lactation records and that for age at first calving and calving interval traits were estimated from 41900 cows’ reproduction records. Data related to Iranian Holstein dairy cattle from 1995 to 2003. Data were analyzed, using univariate and mulitivariate animal model with genetic group effects. The heritability of milk yield, fat yield and fat percentage, age at first calving and calving interval were 0.29 (±0.01), 0.22 (±0.01), 0.38 (±0.01), 0.055 (±0.01) and 0.034 (±0.005), respectively. The linear regression of average breeding values and phenotypic values of cows for a trait on birth year provided an estimate of genetic and phenotypic trend, respectively. There genetic trend for milk yield, fat yield and Calving Interval were 53.5 kg, 1.07kg and 0.46 day, respectively. Their trend for fat percentage and Age at First Calving were -0.011% and -0.23 day, respectively. There was a positive phenotypic trend for milk yield, fat yield and fat percentage. This trend was negative for Age at First Calving and Calving Interval.

To estimate (co) variance components and investigate genetic and phenotypic trend for reproduction traits in Moghani sheep, data were used from Moghani sheep animal breeding station during 1994-2008. Genetic parameters and correlation between traits were estimated with univariate and bivariate animal models using restricted maximum likelihood (REML) method. Phenotypic and genetic trends calculated using regression of phenotypic and genetic average value on birth year of ewe. Direct heritability of age at first lambing was 0.344, heritability and repeatability of number and weight of born and weaned lamb were at ranged of 0.038 to 0.066 and 0.085 to 0.134 respectively, and direct genetic correlations among studied traits were positive and ranged from 0.61 for TLWB-TLWW to 0.98 NLB-NLW. Also, correlation between ages at first lambing with other traits estimated low to moderate. Genetic trend was estimated -0.018 (year) for age at first lambing, 0 for number of born and weaned lamb, -0.001 (kg) for weight of born lamb and 0.009 (kg) for weight of weaned lamb.

The aim of the this study were to estimate heritability and performance of economic traits in Esfhan native fowls. For this purpose, the collected data included 72785 records of 17 generations (1995-2015) were used. Genetic parameters were estimated by multi-trait animal model using WOMBAT software and breeding values were predicted for all birds. Also, a series of six different animal models were fitted for all traits, and the best model for each trait was chosen based on log-likelihood ratio tests (LRT). Results showed that maternal effects are significant in all economic traits except egg number and egg weight at 1st day of laying and so, the inclusion of these effects in models, increasing the accuracy of the estimates of direct heritability. Therefore, it must be taken in to consideration for future selections program, to analysis the traits under selection, by most appropriate model. The highest and lowest heritability were estimated for body weight at 1 day of age (0. 56± 0. 01) and egg weight at 1st day of laying (0. 12 ± 0. 01), respectively. Genetic trends of BW1, BW8, BW12, ASM, WSM, EN, EW1 and MEW were estimated, 0. 07, 12. 35, 17. 36,-1. 36, 23. 40, 1. 06,-0. 03 and 0. 17 respectively, all of these traits were significant. Results of this study showed that there is a possibility of improving traits using appropriate methods of genetic selection.

Accurate prediction of river flow is one of the most important factors in surface water recourses management especially during floods and drought periods. In fact deriving a proper method for flow forecasting is an important challenge in water resources management and engineering. Although, during recent decades, some black box models based on artificial neural networks (ANN), have been developed to overcome this problem and the accuracy privilege to common statistical methods (such as auto regression and moving average time series method) have been shown. However these types of models are implicit and complex in proper network design and can not be simply used by other investigators. In this research the genetic programming (GP) model has been developed as an explicit method for river flow prediction and has been used for investigation the effect of daily discharge trend in Absardeh river flow forecasting. The results have been compared with artificial neural network technique. The results indicated that the proposed GP method performed quite well compared to artificial neural network method and is applicable for river flow prediction.

In the present research some reproductive traits in Holstein cows of Khoramdareh agro-industry were studied. The data set comprised of 10, 891 records collected from 23 years (1988 to 2010) by animal breeding centre, Iran. The total number of animals in the pedigree file was 16/087. Genetic and phenotypic parameters using soft WOMBAT software and restricted maximum likelihood method under animal models and a multivariate trait was estimated. Genetic trend for each trait was estimated using SPSS programmer based upon regression of mean breeding value on year of calving. Phenotypic trend was also estimated based upon regression of phenotypic mean of the trait on year of calving. Levels of heritability from linear model for the traits age at first insemination (AFS), age at first calving (AFC), number of insemination resulted in conception (NSPC), open days (OD), pregnancy rate (PR), calving interval (CI), Non return rate to 90 days (NRR90) respectively 0.037, 0.016, 0.01, 0.032, 0.021, 0.051 and 0.034, respectively. Maximum genetic correlation between traits and open days and pregnancy rates between insemination number of days open and pregnancy, respectively, leading to -0.99 and 0/84 and the lowest genetic correlation between PR with NRR90 (0.001) respectively. Open days and pregnancy rate with the open days and NRR9 respectively Olsen -0.99 and 0.00 the highest and lowest phenotypic correlation showed. Genetic trend for the reproduction traits was between -0/009 for NRR90 and -0/04 for CI respectively. Phenotype trend for the about traits has lower trend for (0/002) NRR90 and (-0/74) for AFS respectively. Lower among environmental trend (-0/007) for NSPC and upper among environmental trend for AFS (-6/52) respectively.

Developments for investigation in the area of artificial intelligence and machine learning, especially in the field of evolutionary computation not only enabled us for having more effective analysis of data, but also providing the ability to use it for understanding any underlying model of financial markets. Economists, statisticians, and finance teachers were always interested in the development and experiment of stock price behavioral models. XCS is a compound system of genetic algorithm and reinforcement learning, which has on-line interaction with the environment and the ability of learning from its own experience. In this study we will provide a model which predicts the movements of next day‘s stock price on one of the corporations in Tehran stock exchange based on historical data and different technical indicators by using XCS. Then, efficiency of the proposed model was measured in comparison with the random walk model. Results showed that the proposed model has more predicting accuracy in comparison with that random walk model.