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	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
2	Proceeding paper 2022 Renato Tinós, Michał Przewoźniczek, Darrell Whitley, Iterated local search with perturbation based on variables interaction for pseudo-boolean optimization. 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. 296-304. ISBN: 978-1-4503-9237-2 Resources:DOIURL
3	Proceeding paper 2022 Michał Przewoźniczek, Renato Tinós, Bartosz Frej, Marcin M Komarnicki, On turning black - into dark gray-optimization with the direct empirical linkage discovery and partition crossover. 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. 269-277. ISBN: 978-1-4503-9237-2 Resources:DOIURL
4	Article 2022 Marcin M Komarnicki, Michał Przewoźniczek, Halina Kwaśnicka, Krzysztof Walkowiak, Incremental recursive ranking grouping for large scale global optimization. IEEE Transactions on Evolutionary Computation. 2022, s. 1-15. ISSN: 1089-778X; 1941-0026 Resources:DOIURL
5	Proceeding paper 2021 Michał Przewoźniczek, Marcin M Komarnicki, Dirk Thierens, Bartosz Frej, Ngoc Hoang Luong, Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms. W: GECCO’21 Companion : Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, July 10-14, 2021 Lille, France / eds. Francisco Chicano, Krzysztof Krawiec. New York, NY : ACM, cop. 2021. s. 1442-1450. ISBN: 978-1-4503-8351-6 Resources:DOI
6	Proceeding paper 2021 Michał Przewoźniczek, Marcin M Komarnicki, Bartosz Frej, Direct linkage discovery with empirical linkage learning. W: GECCO’21 Companion : Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, July 10-14, 2021 Lille, France / eds. Francisco Chicano, Krzysztof Krawiec. New York, NY : ACM, cop. 2021. s. 609-617. ISBN: 978-1-4503-8351-6 Resources:DOI
7	Proceeding paper 2021 Michał Przewoźniczek, Piotr Dziurzanski, Shuai Zhao, Leandro Soares Indrusiak, Multi-objective parameter-less population pyramid in solving the real-world and theoretical problems. W: GECCO’21 Companion : Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, July 10-14, 2021 Lille, France / eds. Francisco Chicano, Krzysztof Krawiec. New York, NY : ACM, cop. 2021. s. 41-42. ISBN: 978-1-4503-8351-6 Resources:DOI
8	Proceeding paper 2021 Michał Przewoźniczek, Marcin M Komarnicki, Fitness caching - from a minor mechanism to major consequences in modern Evolutionary Computation. W: 2021 IEEE Congress on Evolutionary Computation (CEC), 28.06-1.07.2021, Kraków, Poland : proceedings / eds. Jacek Mańdziuk and Hussein Abbass. Danvers : IEEE, cop. 2021. s. 1785-1791. ISBN: 978-1-7281-8394-7; 978-1-7281-8393-0 Resources:DOIURL
9	Article 2021 Michał Przewoźniczek, Marcin M Komarnicki, Empirical problem decomposition - the key to the evolutionary effectiveness in solving a large-scale non-binary discrete real-world problem. Applied Soft Computing. 2021, vol. 113, art. 107864, s. 1-17. ISSN: 1568-4946; 1872-9681 Resources:DOIURLSFX
10	Article 2021 Michał Przewoźniczek, Piotr Dziurzanski, Shuai Zhao, Leandro Soares Indrusiak, Multi-objective Parameter-less Population Pyramid for solving industrial process planning problems. Swarm and Evolutionary Computation. 2021, vol. 60, art. 100773, s. 1-17. ISSN: 2210-6502; 2210-6510 Resources:DOISFX

All publications