E-mail: michal.przewozniczek@pwr.edu.pl

Unit: Faculty of Information and Communication Technology (N) » Department of Systems and Computer Networks

ul. I. Łukasiewicza 3/5, 50-371 Wrocław
building B-4, room 4.14
phone +48 71 320 3207

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Selected publications
1	Article 2024 Michał Przewoźniczek, Bartosz Frej, Marcin M Komarnicki, From direct to directional variable dependencies – non-symmetrical dependencies discovery in real-world and theoretical problems. IEEE Transactions on Evolutionary Computation. 2024, s. 1-15. ISSN: 1089-778X; 1941-0026 Resources:DOIURL
2	Article 2024 Renato Tinós, Michał Przewoźniczek, Darrell Whitley, Francisco Chicano, Iterated local search with Linkage Learning. ACM Transactions on Evolutionary Learning and Optimization. 2024, vol. 4, nr 2, art. 7, s. 1-29. ISSN: 2688-3007 Resources:URLSFX
3	Proceeding paper 2024 Marcin M Komarnicki, Michał Przewoźniczek, Renato Tinós, Xiaodong Li, Overlapping cooperative co-evolution for overlapping large-scale global optimization problems. W: GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, July 14-18, 2024, Melbourne, Australia / ed. Julia Handl. New York, NY : ACM, cop. 2024. s. 665-673. ISBN: 979-8-4007-0494-9 Resources:DOIURL
4	Proceeding paper 2024 Łukasz Tulczyjew, Michał Przewoźniczek, Renato Tinós, Agata M Wijata, Jakub Nalepa, CANNIBAL unveils the hidden gems: hyperspectral band selection via clustering of weighted variable interaction graphs. W: GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, July 14-18, 2024, Melbourne, Australia / ed. Julia Handl. New York, NY : ACM, cop. 2024. s. 412-421. ISBN: 979-8-4007-0494-9 Resources:DOIURL
5	Proceeding paper 2023 Michał Przewoźniczek, Renato Tinós, Marcin M Komarnicki, First improvement hill climber with linkage learning - on introducing dark Gray-box optimization into statistical linkage learning genetic algorithms. W: GECCO '23 : Proceedings of the 2023 Genetic and Evolutionary Computation Conference, July 15-19, 2023, Lisbon, Portugal / eds. Sara Silva, Luís Paquete. New York, NY : ACM, cop. 2023. s. 946-954. ISBN: 979-8-4007-0119-1 Resources:DOI
6	Proceeding paper 2023 Michał Przewoźniczek, Marcin M Komarnicki, To slide or not to slide? Moving along fitness levels and preserving the gene subsets diversity in modern evolutionary algorithms. W: GECCO '23 : Proceedings of the 2023 Genetic and Evolutionary Computation Conference, July 15-19, 2023, Lisbon, Portugal / eds. Sara Silva, Luís Paquete. New York, NY : ACM, cop. 2023. s. 955-962. ISBN: 979-8-4007-0119-1 Resources:DOI
7	Proceeding paper 2023 Renato Tinós, Michał Przewoźniczek, Darrell Whitley, Francisco Chicano, Genetic algorithm with linkage learning. W: GECCO '23 : Proceedings of the 2023 Genetic and Evolutionary Computation Conference, July 15-19, 2023, Lisbon, Portugal / eds. Sara Silva, Luís Paquete. New York, NY : ACM, cop. 2023. s. 981-989. ISBN: 979-8-4007-0119-1 Resources:DOI
8	Proceeding paper 2023 Marcin M Komarnicki, Halina Kwaśnicka, Michał Przewoźniczek, Krzysztof Walkowiak, Incremental recursive ranking grouping - a decomposition strategy for additively and nonadditively separable problems. W: GECCO’23 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion, July 15-19, 2023, Lisbon, Portugal / ed. Luís Paquete. New York, NY : ACM, cop. 2023. s. 27-28. ISBN: 979-8-4007-0120-7 Resources:DOI
9	Article 2023 Marcin M Komarnicki, Michał Przewoźniczek, Halina Kwaśnicka, Krzysztof Walkowiak, Incremental recursive ranking grouping for large scale global optimization. IEEE Transactions on Evolutionary Computation. 2023, vol. 27, nr 5, s. 1498-1513. ISSN: 1089-778X; 1941-0026 Resources:DOIURLSFX
10	Proceeding paper 2022 Michał Przewoźniczek, Marcin M Komarnicki, Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem. W: GECCO '22 : Proceedings of the 2022 Genetic and Evolutionary Computation Conference, July 9-13, 2022, Boston, Massachusetts / ed. Jonathan E. Fieldsend. New York, NY : ACM, cop. 2022. s. 35-36. ISBN: 978-1-4503-9237-2 Resources:DOIURL

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