Di Francescomarino et al. 2018 — GA Hyperparameter Optimisation for PPM

Addresses the recurring PPM critique that no single technique dominates all datasets. Proposes a framework that chains multiple PPM techniques and auto-configures them per-dataset via genetic algorithm HPO.

Contribution

Unified framework covering the PPM pipeline (encoding → model → training).
Two automatic HPO layers — per-step technique selection + per-technique hyperparameter tuning.
Evaluated on two real-life logs; scalable and competitive with hand-tuned baselines.

Significance

Part of the configurability / reproducibility strand of PPM research. Directly addresses the gripe behind the first sentence of Tax et al. 2017 ("relative accuracy is highly sensitive to the dataset at hand").

Cited by

sources/2022-riess-metaheuristics-concept-drift-survey — Riess cites Di Francescomarino et al. as the BPM-adjacent anchor for genetic-algorithm HPO and as the closest PPM work addressing earliness (incorporated as a term in the GA fitness function).
sources/2023-riess-temporal-loss-remaining-cycle-time — Riess contrasts the GA-based HPO approach (manipulating hyper-parameters to improve earliness) against his own loss-function approach (modifying the training objective directly).

Connections

Concepts: concepts/predictive-process-monitoring · concepts/outcome-prediction Authors: entities/chiara-di-francescomarino · entities/marlon-dumas · entities/fabrizio-maggi