  While utilization of digital agents to support crucial decision making is
increasing, trust in suggestions made by these agents is hard to achieve.
However, it is essential to profit from their application, resulting in a need
for explanations for both the decision making process and the model. For many
systems, such as common black-box models, achieving at least some
explainability requires complex post-processing, while other systems profit
from being, to a reasonable extent, inherently interpretable. We propose a
rule-based learning system specifically conceptualised and, thus, especially
suited for these scenarios. Its models are inherently transparent and easily
interpretable by design. One key innovation of our system is that the rules'
conditions and which rules compose a problem's solution are evolved separately.
We utilise independent rule fitnesses which allows users to specifically tailor
their model structure to fit the given requirements for explainability.
