Description:
<jats:title>Abstract</jats:title>
<jats:p>The pragmatic aim of <jats:italic>this paper</jats:italic> is to <jats:italic>provide</jats:italic> a genetic algorithm for predicting the technical systems state. The research <jats:italic>novelty</jats:italic> is to represent the genuine approach to forecast the technical systems states. The given approach implies finding future values by extrapolating current observation results. Forecast can be considered as a diagnostic control at zero-time extrapolation, or as a general case of diagnosis. The developed genetic algorithm is based on the classical representation of genetic algorithms with the changes required for forecasting. So, a function that validates alternative solutions outlaying from the geometric representation of the average values of the time series plot is exploited as a fitness function. The method based on the use of Shewhart process-behavior charts is also applied to exclude failures of the sensor collecting measured data and to control the mutation. The algorithm performs a prediction for one time interval ahead for processes that are not affected by external factors or processes, or the influence of external factors on which is not significant within one time interval. Our experiment confirmed the efficiency of the suggested algorithm. It resulted in obtaining a predictive solution.</jats:p>