Authors S.S. Alkhasov, A.N. Tselykh, A.A. Tselykh
Month, Year 10, 2016 @en
Index UDC 004.891.2
DOI 10.18522/2311-3103-2016-10-110122
Abstract This paper presents a new integrated approach to the implementation of the classification by means of artificial neural networks using genetic algorithms. An overview of previously developed methods of modifying the artificial neural networks based on genetic algorithms (using the inversion operator; divided into some subpopulations; with varying population size) is outlined. We describe the method of modification of the traditional neural network classification algorithm. The modification consists of two stages. The first stage is the detection of suboptimal (optimal for specified conditions) architecture of artificial neural networks (number of neurons in the hidden layer, training algorithm, learning rate, type of activation function, etc.). The second stage is the adjustment of the weight coefficients and biases towards the minimum fitness function of the genetic algorithm. We also study the influence of the input variables to the value of the classification error. In the final part of the paper we show the comparative results of traditional and new advanced approaches. The following parameters of the artificial neural network were obtained: a set of input features (12 of 13), the number of neurons in the hidden layer (46), the learning rate (0.1416), type of activation function (logistic function), training algorithm (algorithm of Levenberg – Marquardt). The advanced neural network model has the average estimation of mean-square error (MSE) which is better than the estimation of the conventional neural network based on gradient methods of the optimization approximately in two times (0.08 vs 0.15). However time of the classification execution is about ten times greater than the duration of the work of traditional artificial neural network. Thus, the feasibility of using artificial neural network in the combination with the optimization of its architecture by using the genetic algorithm is determined by the current accuracy requirement specified by the end user. However, this approach allows more flexibility to work with input datasets in the conditions when their internal structure changes periodically. Therefore, it is recommended to use the developed integrated model if you have access to distributed computing resources.

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Keywords Classification; artificial neural networks; genetic algorithms.
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