Application of Artificial Neural Networks in Genetic Algorithm Control Problems

Abstract

In contemporary intelligent decision support systems, there is still a problem associated with increasing the performance speed of the structural-parametric synthesis of large discrete systems with a given behavior based on genetic algorithms. Currently, there are two main research areas that are designed for mathematical or hardware performance speed improvement. One way to improve hardware performance speed is the use of parallel computing, which includes general-purpose computing on graphics processing units (GPGPU). This article deals with the possibility of improving the performance speed of intelligent systems using the mathematical tool of artificial neural networks by introducing a control module of the genetic algorithm directly when performing the synthesis of solutions. Control of the structural-parametric synthesis process is achieved by predicting and evaluating the state of the genetic algorithm (convergence, attenuation, finding the population in local extremes) using artificial neural networks. This allows changing the operating parameters directly in the course of decision synthesis, changing their destructive ability relative to the binary string, which leads to a change in the trajectory of the population in the decision space, and as a result, should help to improve the performance speed of intelligent decision support systems.

Model of an Artificial Neural Network for Solving the Problem of Controlling a Genetic Algorithm Using the Mathematical Apparatus of the Theory of Petri Nets

“…In this paper, an approach based on the theory of Petri nets was first proposed. The use of this mathematical tool will allow you to fully reveal the properties of parallelism [15,16] that are inherent in the genetic algorithm and artificial neural network [17,18], and it will also allow you to use parallel computing in software implementation. We should remember that the mathematical apparatus of the theory of Petri nets was created as a tool aimed at modeling computer technology, which allows us to talk not only about the possibility of software implementation of the proposed models and methods, but also about the hardware implementation.…”

“…Figure 4. Example of an artificial neural network.Based on the representation(16) and Figure4, the model of an artificial neural network based on a Petri net can be represented as follows (see Figure5).According to (16), we have the following:-Ps = {P0, P1, P2, P3} (P3-correction neuron); -PR = {P10, P11}; -PW = {P4, P5, P6, P7, P8, P9, P18, P19}; -PB = {P12, P13, P17}; -Pout = {P16}; -PNcont = <{P20, P21, P22, P23, P24, P25, P26, P27, P31, P32, P33, P34, P35}, {T0, T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15}, {Ling, Lf}, M0,Pcont>;-M0 = {1,1,1,1,1,1,1,1,1,1,0,0,1,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}.…”

Neuro-Fuzzy based MPPT for Solar PV Panel

“…Through testing of the ANFIS model, an RMSE result of 2.7843e-05 is yielded. A study [15]using fuzzy logic for automation of irrigation schedule and water flow regulation showed an error of 3.86%.Artificial Neural Network is used in intelligent systems to significantly improve the accuracy and prediction rate by means of classifying multiple sets of data e.g., academic placements [16] and GA control system [17].…”

Gaussian Processes Regression based Energy System Identification of Manufacturing Process for Model Predictive Control

“…We can include the Gaussian Processes(GP) models into a class of a nonparametric method of nonlinear system identification where new predictions of system behaviour are computed through the use of Bayesian inference techniques applied to empirical data [11]. GP models can be considered as a new approache such as Support Vector Machines [13]- [14]. In addition, GPs make possibile to include various kinds of prior knowledge into the model [15] for the incorporation of local models and the static characteristic.…”