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Tutorials |
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M.W. Przewozniczek, M. M. Komarnicki Modern
Linkage Learning Techniques in Combinatorial Optimization |
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2021 |
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Most
important and recent publications |
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Journals |
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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, 2021 (in press) |
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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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M.W. Przewozniczek, R, Goścień,
P. Lechowicz, K. Walkowiak,
Metaheuristic
Algorithms with Solution Encoding Mixing for Effective Optimization of SDM
Optical Networks, Engineering
Applications of Artificial Intelligence, 2020 (in press). |
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Conferences |
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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, 2021 (in press). |
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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,
2021 (in press). |
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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), 2021 (in press). |
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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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