Tutorials

 

 

 

 

 

 

M.W. Przewozniczek, M. M. Komarnicki

Modern Linkage Learning Techniques in Combinatorial Optimization

2021

 

 

 

 

 

 

 

 

 

 

Most important and recent publications

 

 

 

 

 

Journals

 

 

 

 

 

 

 

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)

 

 

 

M.W. Przewozniczek, M. M. Komarnicki, Empirical linkage learning, IEEE Transactions on Evolutionary Computation,  vol. 24, no. 6, pp. 1097-1111, 2020.

 

 

 

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.

 

 

 

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.

 

 

 

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).

 

 

 

MORE…

 

 

 

 

 

 

 

Conferences

 

 

 

 

 

 

 

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).

 

 

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).

 

 

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).

 

 

 

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, 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.

 

 

 

MORE…