Efficient Self-Adaptation through Explanation-Driven White-Box Optimization

Self-adaptive systems increasingly rely on black-box predictive models, such as Neural Networks, for decision-making and adaptation planning. In this case, adaptation decisions and their potential impact on the surrounding environment are hard to explain. Further, the opaque nature of these models often involves expensive optimization techniques. The computational complexity is a consequence of the inability to directly observe or comprehend the internal mechanisms of the black-box predictive models. This requires iterative methods to explore a possibly large search space and optimize according to many objectives, creating a critical challenge in balancing effectiveness and cost. In this paper, we propose explanation-driven self-adaptation, a novel approach that embeds model-agnostic interpretable machine learning techniques into the feedback loop. In particular, we present XDA-II, an extended version of XDA that integrates multiple explanation sources. This new version combines Partial Dependence Plots and Feature Importance to maintain interpretability while boosting efficiency. Beyond interpretability benefits, explanations become instrumental to guide adaptations through white-box optimization which is more cost-effective than black-box optimization due to the transparency and predictable mathematical properties of the functions involved. Our empirical evaluation using six study subjects shows that XDA-II achieves more efficient convergence towards optimal adaptation solutions compared to four selected baseline methods including existing black-box methods—random search, NSGA-III, and FITEST—and white-box methods—the predecessor XDA.