Evolutionary algorithms are nature-inspired learning, search and optimization methods that take the
natural evolutionary process of a species as a model to adapt in the best possible way to
environmental conditions. Methodical developments in combination with constantly increasing
computing resources lead to the fact that more and more complex and higher-dimensional tasks can
be solved with evolutionary algorithms. However, almost all of the currently considered optimization
and machine learning tasks are stationary which means that the optimization or modeling goal does
not change during an algorithm run.
This project deals with different methodological approaches to move into the non-stationary domain.
Although so far applied almost exclusively to stationary tasks, evolutionary approaches are an ideal
starting point, since natural evolution itself is highly non-stationary. A species that loses its
adaptability to new environmental conditions by adapting too greedily to currently prevailing
conditions would become extinct just as well at this would happen if environmental conditions
change too rapidly for a species to adapt. A sufficiently high presence of genetic diversity is therefore
just as important in nature in terms of adaptability as it is for non-stationary optimization and
modeling.
Involving biological expertise, the Austrian project group will research implicit methods such as self-
adaptive process extensions that maintain adaptability through a constant support of new genetic
diversity without having to abandon what has already been learned. Age-layered population
structures represent a promising methodological starting point here. So far, approaches of this kind
have been mainly used to reduce premature convergence by a constant supply of new genetic
diversity via the young age layers. In the course of the present project, these approaches will be
extended in the direction of permanent adaptation to changing goals and framework conditions, and
concepts will be explored as to how and which strategic method information can be transferred from
the older to the younger age layers. The cooperating group from the Siberian State University of
Science and Technology will go in the same direction with explicit approaches that change algorithm
parameters or switch between algorithms at runtime based on fitness indicators.