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Tutorials |
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M.W. Przewozniczek, M. M. Komarnicki Modern
Linkage Learning Techniques in Combinatorial Optimization’22 |
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2022 |
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M.W. Przewozniczek, M. M. Komarnicki Modern
Linkage Learning Techniques in Combinatorial Optimization’21 |
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2021 |
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Most
important and recent publications |
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Journals |
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M. M. Komarnicki,
M. W. Przewozniczek, H. Kwasnicka
and K. Walkowiak, Incremental Recursive
Ranking Grouping for Large Scale Global Optimization, IEEE Transactions on Evolutionary
Computation, 2022. (in press) |
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M. W. Przewozniczek,
M. 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,
2021. |
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M. W. Przewozniczek, P. Dziurzanski,
S. Zhao, L. S. Indrusiak, Multi-Objective
Parameter-less Population Pyramid for Solving Industrial Process Planning
Problems, Swarm and Evolutionary
Computation, vol. 60, 2021. |
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M.W. Przewozniczek,
M. M. Komarnicki, Empirical linkage
learning, IEEE Transactions on
Evolutionary Computation, vol. 24,
no. 6, pp. 1097-1111, 2020. |
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M. W. Przewoźniczek, Subpopulation
initialization driven by linkage learning for dealing with the
Long-Way-To-Stuck effect, Information
Sciences, vol. 521, pp. 62-80, 2020. |
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M. W. Przewozniczek, R. Datta, K. Walkowiak, M. Komarnicki, Splitting
the fitness and penalty factor for temporal diversity increase in practical
problem solving, Expert Systems
With Applications, vol. 145, pp.1-11, 2020. |
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Conferences |
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M. W. Przewozniczek,
R. Tinos, 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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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S. Wozniak, M. W. Przewozniczek,
and 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. |
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