ML-Methods for Identifying Features of Glob. Optimization

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.

No additional funding sources recorded.