Risk-return tradeoff and its analysis in alternative investments as a classic goal of finance have been the main subject of many researches in financial management. The use of technical indicators is a portfolio management tools. This research aims to use these indicators in mining stocks trading rules. The period of investigation is from beginning of 1388 until the end of 1393 and the sample of study is including 216 companies listed in TSE. In the period from 1388 to 1390 by using technical indicators and GENETIC algorithm with aim for maximize return and minimize risk, we obtain a model for portfolio optimization and in the period from 1391 to 1393 this model was used in portfolio management. In order to evaluate this model, the results were compared with the market index and found that by using technical indicators can outperform the market.

The main purpose of this paper is to solve an inverse random differential equation problem using evolutionary ALGORITHMS. Particle Swarm Algorithm and GENETIC Algorithm are two ALGORITHMS that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorithm and the GENETIC algorithm, we solve them. The ALGORITHMS presented in this article have advantages over other old methods that have been presented so far. Implementing these ALGORITHMS is simpler, have less run time and produce better approximation. The numerical results obtained in this paper also show that the solutions obtained for the examples presented in the numerical results section are highly accurate and have less error. All of the ALGORITHMS in this paper to obtain the desired numeric results, have been implemented on the Pentium (R) Dual core E5700 processor at 3. 00 GHz.

Scheduling is vital and significant for any event and operation in any system. Thus, there is a need to use all resources efficiently and effectively. In this paper, a new mathematical model of a manpower scheduling problem is presented, which is a class of production planning, in such a way that costs are minimized and resource utilization is maximized. In this proposed model, three main elements are taken into consideration, as follows: personnel, work shift and the associated costs. In this manpower scheduling problem, a number of parameter settings and different variables are defined in order to solve the problem. Due to its NP-hard problem, it is difficult to solve such a problem by traditional optimization tools and available computer packages in reasonable computational time. Thus, in this paper, a meta heuristic method, based on GENETIC ALGORITHMS, is proposed. To evaluate the efficiency of the proposed method, a number of test problems are carried out and the associated results are reported.

In this paper, the performance of semi-active viscous dampers in reducing the response of tall buildings to earthquake acceleration is optimized using GENETIC ALGORITHMS. Torsional eects due to irregularities exist in the building and due to unsymmetrical placement of the dampers are taken into account through 3-D modeling of the building. For the numerical example, a twelve-story building is chosen. The building is modeled as a 3-D frame. The equations of motion of the building with semi-active viscous dampers, subjected to earthquake acceleration, is written, resolved in state space and the results are compared with those of the uncontrolled building. Moreover, in order to minimize building responses such as top story displacement and base shear, the required number and location of dampers are optimized using GENETIC ALGORITHMS.

GENETIC Algorithm is known as a generalized method of stochastic search and has been successfully applied to various types of optimization problems. By GA s it is expected to improve the solution at the expense of additional computational effort. One of the key points which control the accuracy and convergence rate of such a process is the selected method of coding/decoding of the original problem variables and the discrete feasibility space to be searched by GAS. In this paper, a direct index coding (DIC) is developed and utilized for the discrete sizing optimization of structures. The GA operators are specialized and adopted for this kind of encoded chromosomes and are compared to those of traditional GA S. The well-known 10-bar truss example from literature is treated here as a comparison benchmark, and the outstanding computational efficiency and stability of the proposed method are illustrated. The application of the proposed encoding method is not limited to truss structures and can also be directly applied to frame sizing problems.

Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic ALGORITHMS used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic ALGORITHMS used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where GENETIC algorithm (GA) and particle swarm optimization (PSO) ALGORITHMS were combined with each other. The experimental results of simulation are shown that the proposed algorithm optimizes the average makespans of the HEFT_UpRank, HEFT_DownRank, HEFT_LevelRank and MPQMA for 100 independent task graphs scheduling with 10, 50 and 100 tasks. Total optimization of makespans by the proposed algorithm against the other ALGORITHMS were 6.44, 10.41, 6.33 and 4.8 percent respectively.

Tuned mass dampers (TMDs) systems are one of the vibration controlled devices used to reduce the response of buildings subject to lateral loadings such as wind and earthquake loadings. Although TMDs system has received much attention from researchers due to their simplicity, the optimization of properties and placement of TMDs is a challenging task.Most research studies consider optimization of TMDs properties. However, the placement of TMDs in a building is also important. This paper considers optimum placement as well as properties of TMDs. GENETIC ALGORITHMS (GAs) is used to optimize the location and properties of TMDs. Because the location of TMDs at a particular floor of a building is a discrete number, it is represented by binary coded GENETIC algorithm (BCGA), whereas the properties of TMDS are best suited to be represented by using real coded GENETIC algorithm (RCGA). The combination of these optimization tools represents a hybrid coded GENETIC algorithm (HCGA) that optimizes discrete and real values of design variables in one arrangement. It is shown that the optimization tool presented in this paper is stable and has the ability to explore an unknown domain of interest of the design variables, especially in the case of real coding parts. The simulation of the optimized TMDs subject to earthquake ground accelerations shows that the present approaches are comparable and/or outperform the available methods.