  This study presents the approach to analyzing the evolution of an arbitrary
complex system whose behavior is characterized by a set of different
time-dependent factors. The key requirement for these factors is only that they
must contain an information about the system; it does not matter at all what
the nature (physical, biological, social, economic, etc.) of a complex system
is. Within the framework of the presented theoretical approach, the problem of
searching for non-linear regression models that express the relationship
between these factors for a complex system under study is solved. It will be
shown that this problem can be solved using the methodology of \emph{genetic
(evolutionary)} algorithms. The resulting regression models make it possible to
predict the most probable evolution of the considered system, as well as to
determine the significance of some factors and, thereby, to formulate some
recommendations to drive by this system. It will be shown that the presented
theoretical approach can be used to analyze the data (information)
characterizing the educational process in the discipline "Physics" in the
secondary school, and to develop the strategies for improving academic
performance in this discipline.
