This paper presents a hybrid approach to developing a short-term traffic flow prediction model. In this approach a primary model is synthesized based on Neural Networks and then the model structure is optimized through Genetic Algorithm. The proposed approach is applied to a rural highway, Ghazvin-Rasht Road in Iran. The obtained results are acceptable and indicate that the proposed approach can improve model accuracy while reducing model structure complexity. Minimum achieved prediction r2 is 0.73 and number of connection links at least reduced 20% as a result of optimization.

This paper describes an algorithm for tiling with polyminoes that consider rotation and/or reflection of figures in the steps of 90o. First, we review the previous parallel algorithms for tiling problems. Next, we propose a hybrid approach that is based on genetic algorithms (GA) and artificial neural networks (ANN). In this approach, the production of new members in GA and their evaluation are performed by a Hopfield neural network. Finally we compare our method with the previous works, and show that our method can produce global minima for many problems. The algorithm can be used for solving a variety of 2D-packing problems.

The area of neuro-optometry, a subspecialty of optometry, has started a long time ago and continued to serve patients with neurological dysfunctions of the visual system through various methods and applications. The efficiency of neuro-optometric rehabilitation has been proved by a wealth of scientific presentations and publications in the area of neuroscience. However, this subspecialty of optometry has not yet been appreciated and recognized in many regions of the world. This scientific article outlines neuro-optometry, neuro-optometrist’ s duties, and neuro-optometric rehabilitative implications in order to highlight the importance of neurooptometric rehabilitation, to shed a light on one of the missing responsibilities of optometrists in the area of neuroscience, to present the significance of references and sources worked in this area of science, and to define relevant duties for optometrists who wish to work in this sector

In the current study, a new hybrid of the genetic algorithm (GA) and adaptive Neuro-fuzzy inference system (ANFIS) was introduced to model the discharge coefficient (DC) of triangular weirs. The genetic algorithm was implemented for increasing the efficiency of ANFIS by adjusting membership functions as well as minimizing error values. To evaluate the proficiency of the proposed hybrid method, the Monte Carlo simulations (MCS) and the k-fold validation method (k=5) was applied. The results of developed hybrid model indicate that the weir vortex angle, flow Froude number, the ratio of the weir length to its height, the ratio of the channel width to the weir length and ratio of the flow head to the weir height are the most effective parameters in the DC estimation. The quantitative examination of the proposed hybrid method indicates that the Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are as 0. 016 and 1. 647 (respectively) for the superior model. Besides, the Froude number is found as the most effective variable in DC modeling through the quantitative analysis. A comparison of the developed hybrid ANFIS-GA with the individual ANFIS model in the DC estimation indicates the hybrid model outperformed than the individual one.

In this paper, an adaptive neuro fuzzy sliding mode based genetic algorithm (ANFSGA) control system is proposed for a pH neutralization system. In pH reactors, determination and control of pH is a common problem concerning chemical-based industrial processes due to the non-linearity observed in the titration curve. An ANFSGA control system is designed to overcome the complexity of precise control of pH. In the proposed control system the genetic algorithm is employed to do the crossover and mutation operation in adaptive neuro fuzzy inference system (ANFIS) mechanism. In this way, on-line learning ability is employed to deal with external disturbance by adjusting the control parameters.  The control objective is to drive the system state to the original equilibrium point or to track the set point.