Conference publications

 

 

 

 

 

M. W. Przewozniczek, F. Chicano, R. Tinós, J. Nalepa, B. Ruszczak, and A. M. Wijata, On Revealing the Hidden Problem Structure in Real-World and Theoretical Problems Using Walsh Coefficient Influence, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25), 2025 (in press).

 

 

S. L. Thomson, M. W. Przewozniczek, Subfunction Structure Matters: A New Perspective on Local Optima Networks, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25), 2025 (in press).

 

 

J. Piątek, M. W. Przewozniczek, F. Chicano, and R. Tinós, Moving between high-quality optima using multi-satisfiability characteristics in hard-to-solve Max3Sat instances, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25), 2025 (in press).

 

 

J. Maciążek, M. W. Przewozniczek, and J. Schwaab, Seeking and leveraging alternative variable dependency concepts in gray-box-elusive bimodal land-use allocation problems, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25), 2025 (in press).

 

 

A. M. Wijata, M. Bieńkowska, M. W. Przewozniczek, J. Nalepa, Genetic Feature Selection for Multimodal Wound Detection, In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '25), ACM, 2025 (in press).

 

 

 

A. M. Wijata, N. Longépé, M. W. Przewozniczek, J. Nalepa, Machine Learning and Genetic Algorithms: An Intricate Relationship for Locating Methane in Satellite Images, In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '25), ACM, 2025 (in press).

 

 

 

M. M. Komarnicki, M. W. Przewozniczek, R. Tinós, and X. Li, Overlapping Cooperative Co-Evolution for Overlapping Large-Scale Global Optimization Problems, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '24), pp. 665–673, 2024.

 

 

L. Tulczyjew, M. W. Przewozniczek, R. Tinós, A. M. Wijata, and J. Nalepa, CANNIBAL Unveils the Hidden Gems: Hyperspectral Band Selection via Clustering of Weighted Variable Interaction Graphs, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '24), pp. 412–421, 2024.

 

 

M. W. Przewozniczek, R. Tinós, M. M. Komarnicki, First Improvement Hill Climber with Linkage Learning -- on Introducing Dark Gray-Box Optimization into Statistical Linkage Learning Genetic Algorithms, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23), pp. 946-954, ACM, 2023.

 

 

R. Tinós, M. W. Przewozniczek, D. Whitley, F. Chicano, Genetic Algorithm with Linkage Learning, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23), pp. 981-989, ACM, 2023 .

 

 

M. W. Przewozniczek, M. M. Komarnicki, To slide or not to slide? Moving along fitness levels and preserving the gene subsets diversity in modern evolutionary algorithms, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23), pp. 955-962, ACM, 2023.

 

 

M. M. Komarnicki, M. W. Przewozniczek, H. Kwasnicka, K. Walkowiak, Incremental Recursive Ranking Grouping - A Decomposition Strategy for Additively and Nonadditively Separable Problems, In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '23), pp. 27-28, ACM, 2023.

 

 

 

R. Tinós, M. W. Przewozniczek, D. Whitley, Iterated Local Search with Perturbation based on Variables Interaction for Pseudo-Boolean Optimization, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '22), pp. 296–304, ACM, 2022.

 

 

M. W. Przewozniczek, R. Tinós, B. Frej,M. M. Komarnicki, On turning Black- into Dark Gray-optimization with the Direct Empirical Linkage Discovery and Partition Crossover, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '22), pp. 269–277, ACM, 2022.

 

 

M. W. Przewozniczek, M. M. Komarnicki, "Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem," in Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion (GECCO '22), pp. 34-35, ACM, 2022.

 

 

M. W. Przewozniczek,M. M. Komarnicki, P. A. N. Bosman, D. Thierens, B. Frej, N. H. Luong, Hybrid Linkage Learning for Permutation Optimization with Gene-pool Optimal Mixing Evolutionary Algorithms, in Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21), ACM, pp. 1442–1450, 2021.

 

 

M.W. Przewozniczek,M. M. Komarnicki, B. Frej, Direct linkage discovery with empirical linkage learning, in Proceedings of the 2021 Genetic and Evolutionary Computation Conference (GECCO ’21), ACM, pp. 609–617, 2021.

 

 

M. W. Przewozniczek, P. Dziurzanski, S. Zhao, L. S. Indrusiak, Multi-Objective Parameter-less Population Pyramid in Solving the Real-World and Theoretical Problems, in Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21). ACM, pp. 41-42, 2021.

 

 

 

M. W. Przewozniczek,M. M. Komarnicki, Fitness caching - from a minor mechanism to major consequences in modern evolutionary computation, in Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1785-1791, 2021.

 

 

 

M. W. Przewozniczek, B. Frej,M. M. Komarnicki, On measuring and improving the quality of linkage learning in modern evolutionary algorithms applied to solve partially additively separable problems, in Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO ’20). ACM, pp. 742–750, 2020.

 

 

 

M. M. Komarnicki, M. W. Przewozniczek,T. Durda, Comparative Mixing for DSMGA-II, in Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO ’20), ACM, pp. 708–716, 2020.

 

 

 

S. Wozniak, M. W. Przewozniczek,M. M. Komarnicki, Parameter-less population pyramid for permutation-based problems, in Proceedings of the Parallel Problem Solving from Nature (PPSN XVI), pp. 418-430, 2020.

 

 

S. Zhao P. Dziurzanski, M. W. Przewozniczek,M. Komarnicki, L.S. Indrusiak, Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories, Proceedings of the Genetic and Evolutionary Computation Conference, (GECCO 19), pp. 1381-1389, 2019.

 

 

 

A. M. Zieliński,M. M. Komarnicki and M. W. Przewozniczek, Parameter-less population pyramid with automatic feedback, Proceedings of the Genetic and Evolutionary Computation Conference Companion, (GECCO 19), pp. 312-313, 2019.

 

 

 

S. Zhao, H. Mei, P. Dziurzanski, M. W. Przewozniczek, L.S. Indrusiak, Cloud-Based Integrated Process Planning and Scheduling Optimisation via Asynchronous Islands, in: Djemame K., Altmann J., Bañares J., Agmon Ben-Yehuda O., Naldi M. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2019. Lecture Notes in Computer Science, Vol. 11819. Springer, Cham, pp. 247-259, 2019.

 

 

 

M. W. Przewozniczek,M. M. Komarnicki, The practical use of problem encoding allowing cheap fitness computation of mutated individuals, Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, (FedCSIS 2019), pp. 57-65, 2018.

 

 

 

M. W. Przewozniczek,M. M. Komarnicki, The influence of fitness caching on modern evolutionary methods and fair computation load measurement, Proceedings of the Genetic and Evolutionary Computation Conference Companion, (GECCO 18), pp. 241-242, 2018.

 

 

 

M. Przewozniczek, Problem encoding allowing cheap fitness computation of mutated individuals, Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp.308-316, 2017.

 

 

 

M. M. Komarnicki M. W. Przewozniczek, Parameter-less population pyramid with feedback, Proceedings of the Genetic and Evolutionary Computation Conference Companion, (GECCO 17), pp. 109–110, 2017.

 

 

 

M. W. Przewozniczek, K. Walkowiak,M. Aibin, The Effectiveness of the Simplicity in Evolutionary Computation, Intelligent Information and Database Systems: 9th Asian Conference, ACIIDS 2017, pp. 392–402, 2017.

 

 

 

M. M. Komarnicki, M. W. Przewozniczek, Linked Genes Migration in Island Models, Proceedings of the 8th International Joint Conference on Computational Intelligence, IJCCI 2016, vol. 3, pp. 30-40, 2016.

 

 

 

M. Przewozniczek, Dynamic Subpopulation Number Control for Solving Routing and Spectrum Allocation Problems in Elastic Optical Networks, Proceedings of the Third European Network Intelligence Conference, ENIC, pp.257-264, 2016.

 

 

 

M. Przewozniczek, Multi population pattern searching algorithm for solving routing spectrum allocation with joint unicast and anycast problem in elastic optical networks, Proceedings of the 16th International Conference on Intelligent data engineering and automated learning - IDEAL 2015, Vol. 42, No. 21, pp.328-339, 2015.

 

 

 

B. Fidrysiak, M. W. Przewozniczek, Towards finding an effective way of discrete problems solving: the particle swarm optimization, genetic algorithm and linkage learning techniques hybrydization, Proceedings of the 7th International Joint Conference on Computational Intelligence, IJCCI 2015, vol. 1, pp. 228-236, 2015.

 

 

 

M. Przewozniczek, Towards finding an effective uniform and single point crossover balance for optimization of Elastic Optical Networks, Proceedings of The Second European Network Intelligence Conference, ENIC 2015, pp.40-46, 2015.

 

 

 

M. Przewoźniczek, K. Walkowiak, Quasi-hierarchical Evolutionary Algorithm for Flow Optimization Survivable MPLS Networks, Proceedings of the 7th International Conference on Computational Science and Its Applications, ICCSA 2007, Lecture Notes in Computer Science, Vol. 4707, Springer Verlag, 2007, s. 330-342, 2007.