Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for the calculation of the risk factor, among which the most important one is the Conditional Value at Risk (CVaR). On the other hand, Data Envelopment Analysis (DEA) can be used to form the optimal portfolio and evaluate its efficiency. In these models, the optimal portfolio is created by stocks or companies with high efficiency. Since the search space is vast in actual markets and there are limitations such as the number of assets and their weight, the optimization problem becomes difficult. Evolutionary algorithms are a powerful tool to deal with these difficulties. The automotive industry in Iran involves international automotive manufacturers. Hence, it is essential to investigate the market related to this industry and invest in it. Therefore, in this study we examined this market based on the price index of the automotive group, then optimized a portfolio of automotive companies using two methods. In the first method, the CVaR measurement was modeled by means of DEA, then Particle Swarm Optimization (PSO) and the Imperial Competitive Algorithm (ICA) were used to solve the proposed model. In the second method, PSO and ICA were applied to solve the CVaR model, and the efficiency of the portfolios of the automotive companies was analyzed. Then, these methods were compared with the classic Mean-CVaR model. The results showed that the automotive price index was skewed to the right, and there was a possibility of an increase in return. Most companies showed favorable efficiency. This was displayed the return of the portfolio produced using the DEA-Mean-CVaR model increased because the investment proposal was basedon the stock with the highest expected return and was effective at three risk levels. It was found that when solving the Mean-CVaR model with evolutionary algorithms, the risk decreased. The efficient boundary of the PSO algorithm was higher than that of the ICA algorithm, and it displayed more efficient portfolios.Therefore, this algorithm was more successful in optimizing the portfolio.

Introduction: Glucose-6-phosphate dehydrogenase deficiency (G6PD) or fauvism is the most common enzyme deficiency in human, so that 400 million people are living with this disease worldwide. This study aimed to investigate the role of some neonatal factors among newborns suffering from G6PD deficiency and neonatal outcomes associated with this disease. Materials and methods: In this study, two methods including case-control and retrospective cohort regarding some neonatal factors associated with G6PD deficiency were used. These methods were performed on 142 children with this kind of deficiency and 142 healthy infants in the city of Marvdasht during 2013-2014. The analysis of data was based on chi-square tests, t-test, logistic regression, descriptive statistics and estimation of odds ratios or relative risks via SPSS16 software. Results: Totally 284 newborns including 132 (46.6%)/ 152 (53.4%) boys/girls and mean weight on birth of 3163 ± 471 (gr) were analyzed. Comparison of case and control samples did not show any significant differences between sex and involving with G6PD deficiency but the chance of having a baby with this defect in pregnancy intervals between 6 to 8 years was increased (95% CI: 1-4.4, OR: 2). Relative risk of jaundice in infected and healthy infants was estimated as 3.73, which demonstrated a statistically significant association (95% CI: 1.33-10.4). Conclusion:The results of this study showed that the number of hospitalization is increased due to jaundice in infants with G6PD. There is also an insignificant relation between low birth weight, rank of birth and type of delivery.