Abstract
Many real-world projects aim at finding optimal solutions to a specific problem and search space. The optimization task can be hard in itself, but often the problem function is not even known. In such cases, it is necessary to experimentally test possible solutions for their appropriateness. In many domains, such as material science, it is expensive and time-consuming to do these tests. Therefore, ML is a technique to bridge this gap and give hints on the performance of a proposed solution. In this talk, I will delve into the problem of surrogate functions, how they can be learned, and how their prediction quality can be used to steer the optimisation process. I will demonstrate this approach using EvoAl, a DSL-based optimisation framework.
Talk in the series “Train Your Engineering Network”.
