In financial studies, portfolio can be defined as a set of investments that are selected and accepted by an individual or institution. Portfolio selection is one of the main concerns of investors in financial markets. The average-variance model with bound restrictions is considered as one of the main models in solving the portfolio optimization problem. In terms of complexity, this model is a polynomials NP-hard non-linear problem that cannot be accurately solved. In this study, an Antlion optimizer- GENETIC algorithm (ALOGA) and a population based incremental learning and differential evolution algorithm (PBILDE), which are modern meta-heuristic models for solving optimization problem, are used to optimize the investment portfolio through increase the return and reduce the risk. Among 591 companies listed on Tehran stock exchange from April 2012 through March 2015, 150 companies were selected as the final sample using screening method. The data of these companies were analyzed using the applied ALGORITHMS in this research and their efficiency was compared together. The results indicate that ALOGA and PBILDE ALGORITHMS both are suitable for solving the portfolio optimization problem. In addition, using the ALOGA algorithm, it is possible to create an optimal portfolio with high accuracy and efficiency.

In recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and GENETIC ALGORITHMS - neural network hybrid model as a successful model to predict earnings management based on adjusted Jones model were used in the Tehran Stock Exchange. The sample used in this study is consisted of 570 firm-year between 2008 to 2013. The results showed that neural networks have a high ability to predict earnings management rather than the adjusted Jones linear model. The findings also suggest that, the GENETIC algorithm through optimizing artificial neural network weights is able to increase power of artificial neural network to predict earnings management.

With increase energy consumption in the world, power network development is necessary but creating a new transmission line is very costly. Therefore increasing line transmission capability is economic. We propose a method to FACTS device to the power network, in this paper and resulted in introducing decrease power network loss's and increase transmission capability with optimal choice and allocation of FACTS device by using GENETIC algorithm. We examine, proving this method by simulation IEEE6 bus system and using the GENETIC algorithm for optimal choice and allocation of FACTS device in this system.

Background and Objectives: Substitution-Box (S-Box) is one of the essential components creating confusion and nonlinear properties in cryptography. To strengthen a cipher against various attacks, including side channel attacks, these boxes need to have numerous security properties. In this paper, a novel S-Box construction method is introduced aimed at improving the resistance of S-Boxes against power analysis attacks. Methods: In the preprocessing phase of this approach, a suitable initial S-Box with some basic security properties was generated by adopting a fast algorithm. Then, in the main stage, using the initial S-Box, we generate new S-Boxes which not only have the properties of the initial S-Box but also have significantly improved under another set of security properties. To do this, new S-Boxes were generated using a GENETIC algorithm on a particular subset of the linear combination set of coordinate functions of the initial S-Box. Results: The performed experiments demonstrated that the values of all security properties of these new S-Boxes, especially the measures of transparency order, signal-to-noise ratio, confusion coefficient, bijection property, fixed point, and opposite fixed points, have been substantially improved. For example, our experiments indicate that 70, 220, 2071, 43, and 406 S-Boxes are found better than the initial S-Box, respectively, in the dimensions of 4×4 through 8×8 Conclusion: In this paper, a new S-Box construction method is introduced where the properties related to side channel attacks are improved, without destroying other security features. Besides, some results obtained from generated S-Boxes in the dimensions of 4×4 through 8×8 demonstrated that the generated S-Boxes are not only improved relative to the initial S-Box, but also in certain cases, considerably better than some well-known S-Boxes.

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.

Due to its biological nature and high dependence on regional condition, agriculture is the largest consumer of water resources in many countries. Hence, today, agricultural water management plays an important role in the use of water resources of these countries. The present study used GENETIC Algorithm to optimize cultivation area, allocate irrigation water, and maximize profits gained from the cultivation of some crops under various weather conditions in Qazvin Plain (located in the north west of the central plateau of Iran), where some of the required water is obtained from Taleghan dam. In this study, different probability levels of rainfall, evaporation, and input flow of optimization were combined under four different weather conditions. The results showed that in normal, wet, dry, and hot - dry weather conditions, the profit earned from the new cultivation pattern introduced by the model was much more than that of the current pattern. Moreover, following this new pattern could mostly result in lower water consumption in this sector, such that the volume of water stored in the dam reservoir at the end of the operation in wet, normal, dry, and hot-dry conditions would increase by, respectively, 262045.2, 2862686.6, 273089 and 955542 m3. The results showed that the cultivation area of sugar beet in every of the four different condition was reduced (over 80%) because of its high water requirement and low yield, therefore, its cultivation is not recommended under any weather conditions in the studied area. Following the new cropping pattern delivered by this model, the farmers' profit in wet, normal, dry, and hot-dry conditions would increase by, respectively, 2.81%, 2.62%, 1.34%, and 1.53% compared to the prevailing pattern.

Value at Risk (VaR) is the maximum loss which could be incurred within a given time horizon, except for a small percentage, that its application has sharply increased after the 90s. Parallel to the increase in usage of value-at-risk in risk management areas, validation of VaR measures has gain great importance. In prevalent back testing approaches, returns which are yielded from VaR estimators are not regarded as a criterion. It's may not be desirable for the investors who emphasize on return more than the risk. What distinguishes this study from other researches in the field of back testing VaR estimation models is the simultaneous consideration of actual return and loss(CVaR) which were yielded from VaR estimators as criteria of risk and return that are the primary basis for financial studies. On the other hand, due to relativeness of risk and return in terms of investors, we considered the weight of these two indexes as fuzzy. In this paper, we constitute and optimize our risky portfolio with safety-first investor's rule. We need to estimate quantile of risky portfolio's return in objective function of safety-first investor's rule to optimize the portfolio. VaR estimators were used to calculate it. On the other hand, given the non- convexity of VaR function and also other reasons, we applied one of the most popular meta-heuristic models namely GENETIC ALGORITHMS for optimization. Our findings show that GEV and HS models are more conservative than parametric models (t-student and normal) and also have better performance in portfolio optimization. The empirical findings also indicate that safety-first investor will choose significantly different amounts of borrowing. Thus, the scale of the risky portfolio and the amount borrowed is diverse across methods. There is another interesting finding. Despite the computational simplicity of historical simulation method, it has shown the best performance of all.

The main idea of this paper is to Adopt the optimal design of the general aviation aircraft wing to reach the optimal range and weight. For this purpose, the non-dominated sorting GENETIC algorithm II; has been used as an optimization tool for reducing three significant elements of aircraft design, including decisions that require a trade-off, time, and cost. The cost function of the optimization was the minimization of wing weight and maximization of aircraft range which was constrained by Four penalty functions and limiting decisions variables. The first function constraint was lift coefficient which should be equal to the lift coefficient required for supporting aircraft weight at cruise flight. The second and the third functions were Taper-Ratio, and tip to root maximum thickness ratio must be between one and zero. The fourth function was to constrain the sum of the absolute value of twist with incidence angle that must be greater or equal to the absolute value of wing zero lift. The fifth penalty function does not allow the lift-to-drag ratio to exceed the maximum limit of the lift-to-drag ratio. Design parameters were root chord, tip chord, wingspan, incidence angle, twist angle, airfoil zero-lift angle of attack, maximum thickness to chord ratio tip, and chord. In the end, the optimal wing shape design was proposed and validated with the target aircraft. The results show that compared to the most efficient target aircraft, 6. 84% improvement in range but 2. 87% increased weight has been achieved in the optimal response to the problem.

Engineering materials and structures have crack-like defects leading to premature failures. The usage of fracture mechanics to assess the structural integrity requires knowledge on the type, location, shape, size, and orientation of the flaws. Tomographic reconstruction is one of the commonly used nondestructive testing methods for flaw characterization. The cross sectional image of the object being tested is obtained through the application of various reconstruction methods that are categorized as either analytical methods or iterative methods. In this work, an iterative algorithm that works on the principles of GENETIC ALGORITHMS is developed and used for the reconstruction. The results of simulation studies on the tomographic reconstructions using GENETIC ALGORITHMS for the identification of defects in isotropic materials are discussed in the paper. The solution methodology based on the use of GENETIC ALGORITHMS is applied to reconstruct the cross sections of test specimens with different flaw characteristics. Simulated time-of-flight data of ultrasound rays transmitted through the specimen under investigation is used as input to the algorithm. The time-of-flight data is simulated neglecting the bending of ultrasound rays and assuming straight ray paths. Numerical studies performed on several specimens with flaws of known materials but unknown location, size and shape are presented. The number of ultrasonic transmitters and receivers needed for complete scanning of the specimen’ s cross section is analyzed and presented. The findings of the parametric analysis and sensitivity analysis in order to choose appropriate range of algorithm parameters for performance quality and robustness of the algorithm are presented. The performance of the present algorithm with noisy projection data is also discussed.

Verticillium dahliae is an economically important pathogen causing vascular wilt on more than 160 plant species. Most strains have a wide host range. Forty-five wild isolates of V. dahlia recovered from different woody and herbaceous plants throughout Iran. GENETIC diversity of the isolates was assessed through vegetative compatibility groups (VCGs) using nitrate non-utilizing (nit) mutants. Nit mutants were generated using water agar supplemented with 5-7% KClO3. Nit mutants from different isolates were used in all possible combinations. Three VC local groups were identified and designated as VCGA, VCGB and VCGC which correspond to VCG2B (88.8%) and VCG 2A (8.8%) and weakly to VCG2A (2.2%) Most of the isolates were assigned to VCG2B. The results indicated that GENETIC diversity of V. dahliae is very low and there were no relationship between VCGs and geographicalorig in of the isolates. Host specificity of V. dahliae using 18 isolates was determined on 11 different hosts plant species. Different plants reacted differently to the isolates and were divided into three groups: very sensitive (eggplant, pistachio, cotton and okra), semi-sensitive (sunflower, radish and rapeseed) and low sensitive (pepper, tomato, small radish and cabbage). Each plant species reacted differently to each isolate. Mint was less sensitive to the pathogen (5 isolates) than other plant species tested.