In this paper, we propose a GENETIC algorithm, called GenSPN, for finding highly probable differential characteristics of substitution permutation networks (SPNs). A special fitness function and a heuristic mutation operator have been used to improve the overall performance of the algorithm. We report our results of applying GenSPN for finding highly probable differential characteristics of Serpent block cipher. A comparison of the resultant characteristics with the previously published works shows that GenSPN can find differential characteristics of higher probabilities.

Labyrinth spillways were considered as one of the appropriate options to pass PMF discharge where the system had difficulties. In a certain width in similar head these spillways with nonlinear crest can pass greater discharges compare to spillways with frontal crest. Since they pass large discharge under low hydraulic heads and need to smaller place in plane compare to other types of spillways, they are considered as economical structures. Therefore it is essential to apply optimum geometry with maximum passing discharge under specific hydraulic conditions with minimum construction cost. For this purpose in this research fuzzy inference system and GENETIC ALGORITHMS to optimize the spillway's geometry which satisfy the hydraulic conditions were used. To apply fuzzy inference system and to evaluate the coefficient based on available input - output pattern, from it ANFIS was employed. In this section in ANFIS model using experimental data input data such as angle of spillway wallsalong the flow (α), ratio of total head to spillway height (Ht/p), and discharge coefficient (Cd) were trained. Finally, using GENETIC algorithm and ANFIS model output, optimum geometry was found with defining the minimum cost function to satisfy appropriate hydraulic conditions.

Evaporation is one of the important factors in the hydrological cycle and is one of the determinants of energy equilibrium at ground level and water balance, which is required in various areas such as hydrology, hydrology, agriculture, forest management, and management of water resources (Sanei Nejad et al., 2011). In this regard, one of the basic data in designing irrigation and drainage networks is the amount of evaporation power in each region. Because the design of transmission networks, such as drainage or drainage channels, as well as other parts of water design, depends on the amount of water required by the evaporation phenomenon (Jahanbakhsh et al., 1380). In general, evaporation hydrology is generally referred to as the phenomenon of water It simply turns steam into a physical process.

In order to design retaining walls, first the initial dimensions of the wall should be estimated. In order to choose these dimensions, the designer should use reasonable proportions that were achieved by previous experiences of the designing different retaining walls. These dimensions are introduced based on a ratio of the height of walls. Current reseasrches showed that by changing the conditions such as properties of the backfill materials of the retaining wall, the local seismic conditions, the height of the wall and limitation in choosing the arbitrarily dimensions etc, these estimated dimensions would not be appropriate for an economical design. In this paper, by means of GENETIC and bees ALGORITHMS, economical dimensions of the wall for static, pseudo static and pseudo dynamic loading conditions will be calculated precisely in a such way that stability of the retaining wall against sliding, overturning and bearing capacity are provided. Also from structural consideration point of view, the designed walls could resist appropriately against the applied forces.

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society. In present study, three stock selection and optimization ALGORITHMS including GENETIC algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this. In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three ALGORITHMS. However, in order to compare the two ALGORITHMS and analysis of superiority of ALGORITHMS, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk. Since the objective function of particle swarm ALGORITHMS was less, in other word, it has the least error and gain the best result so in comparing to other ALGORITHMS it has been performed better which shows the relative superiority of this ALGORITHMS in the selection of the optimal portfolio.

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.In present study, three stock selection and optimization ALGORITHMS including GENETIC algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three ALGORITHMS. However, in order to compare the two ALGORITHMS and analysis of superiority of ALGORITHMS, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.Since the objective function of GENETIC ALGORITHMS was less, in other word, it has the least error and gain the best result so in comparing to other ALGORITHMS it has been performed better which shows the relative superiority of these ALGORITHMS in the selection of the optimal portfolio.