| 2026 | GECCO | Obtaining Partition Crossover masks using Statistical Linkage Learning for solving noised optimization problems with hidden variable dependency structure. | Michal Przewozniczek, Bartosz Frej, Marcin Komarnicki, Michal Prusik, Renato Tins |
| 2026 | GECCO | The hop-like problem nature - unveiling and modelling new features of real-world problems. | Michal Witold Przewozniczek, Bartosz Frej, Marcin Komarnicki |
| 2025 | FOGA | Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-Based Function Concatenations. | Michal Prusik, Bartosz Frej, Michal Witold Przewozniczek |
| 2025 | GECCO | On Defining and Discovering Non-Symmetrical Dependencies. | Michal Witold Przewozniczek, Bartosz Frej, Marcin M. Komarnicki |
| 2022 | GECCO | On turning black - into dark gray-optimization with the direct empirical linkage discovery and partition crossover. | Michal Witold Przewozniczek, Renato Tins, Bartosz Frej, Marcin M. Komarnicki |
| 2021 | GECCO | Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms. | Michal Witold Przewozniczek, Marcin M. Komarnicki, Peter A. N. Bosman, Dirk Thierens, Bartosz Frej, Ngoc Hoang Luong |
| 2021 | GECCO | Direct linkage discovery with empirical linkage learning. | Michal Witold Przewozniczek, Marcin Michal Komarnicki, Bartosz Frej |
| 2020 | GECCO | On measuring and improving the quality of linkage learning in modern evolutionary algorithms applied to solve partially additively separable problems. | Michal Witold Przewozniczek, Bartosz Frej, Marcin M. Komarnicki |