Destructive Ability Analysis of Genetic Algorithm Operator When Solving Business Process Problem of Structural and Parametric Synthesis

Abstract

The work analyzes the destructive ability of operators of a classical genetic algorithm when solving the problem of controlling the trajectory of a population during the search for solutions. In accordance with the hypothesis put forward, when solving the problem of structural-parametric synthesis of large discrete systems with a given behavior, it is advisable to change the settings of the functioning of the genetic algorithm operators to ensure: better convergence of the genetic algorithm, avoid attenuation, signal the need to restart the solution synthesis procedure, and also facilitate the derivation of the population from local extrema. It is proposed to use an artificial neural network as a control element (control add-on), which should implement control of the process of synthesis of simulation models of business processes based on a given behavior (the ability of a simulation model to convert a given input vector into a reference output), which is especially important when working with large systems. In accordance with the same hypothesis, an increase in the destructive ability of the crossing over and mutation operators allows the population to be dispersed across the solution space, which is advisable when attenuation occurs and is in a local extremum, and a decrease in the destructive ability contributes to a more thorough search for solutions in a certain area of the solution space. The paper provides examples of the work of operators and the behavior of the population when synthesizing simulation models of business processes based on the theory of Petri nets. To model the operation of a genetic algorithm and an artificial neural network, it is proposed to use the theory of Petri nets, which simplifies the process of managing the synthesis procedure and allows the use of parallel programming tools with distributed GPGPU computing on CUDA technology. As part of this study, an analysis of the behavior of the population when changing the operating settings of operators was carried out, which confirmed the hypothesis put forward.