| A predictive evolutionary algorithm for dynamic constrained inverse kinematics problems P Filipiak, K Michalak, P Lipinski Hybrid Artificial Intelligent Systems: 7th International Conference, HAIS …, 2012 | 5 | 2012 |
| A review of methods for imbalanced multi-label classification AN Tarekegn, M Giacobini, K Michalak Pattern Recognition 118, 107965, 2021 | 268 | 2021 |
| An evolutionary algorithm with practitioner’s-knowledge-based operators for the inventory routing problem P Lipinski, K Michalak Evolutionary Computation in Combinatorial Optimization: 18th European …, 2018 | 2 | 2018 |
| Analysis of dynamic properties of stock market trading experts optimized with an evolutionary algorithm K Michalak Applications of Evolutionary Computation: 17th European Conference …, 2014 | 1 | 2014 |
| Analysis of inter-rater agreement among human observers who judge image similarity K Michalak, B Dzieńkowski, E Hudyma, M Stanek Computer Recognition Systems 4, 249-258, 2011 | 1 | 2011 |
| Auto-adaptation of genetic operators for multi-objective optimization in the firefighter problem K Michalak Intelligent Data Engineering and Automated Learning–IDEAL 2014: 15th …, 2014 | 18 | 2014 |
| Channel Configuration for Neural Architecture: Insights from the Search Space SL Thomson, G Ochoa, N Veerapen, K Michalak Proceedings of the Genetic and Evolutionary Computation Conference, 1267-1275, 2023 | 2 | 2023 |
| Classifier-based evolutionary multiobjective optimization for the graph protection problem K Michalak Applied Soft Computing 131, 109721, 2022 | 1 | 2022 |
| Continuous population-based incremental learning with mixture probability modeling for dynamic optimization problems A Lancucki, J Chorowski, K Michalak, P Filipiak, P Lipinski Intelligent Data Engineering and Automated Learning–IDEAL 2014: 15th …, 2014 | 2 | 2014 |
| Correlation based feature selection method K Michalak, H Kwasnicka International Journal of Bio-Inspired Computation 2 (5), 319-332, 2010 | 61 | 2010 |
| Correlation dimension and the quality of forecasts given by a neural network K Michalak, H Kwasnicka Conference on Computability in Europe, 332-341, 2005 | 2 | 2005 |
| Correlation-based feature selection strategy in classification problems K Michalak, H Kwaśnicka International Journal of Applied Mathematics and Computer Science 16 (4 …, 2006 | 69 | 2006 |
| Correlation-based feature selection strategy in neural classification K Michalak, H Kwasnicka Sixth international conference on intelligent systems design and …, 2006 | 45 | 2006 |
| Cross-validation approach to evaluate clustering algorithms: An experimental study using multi-label datasets AN Tarekegn, K Michalak, M Giacobini SN Computer Science 1, 1-9, 2020 | 18 | 2020 |
| Crossover Operator Using Knowledge Transfer for the Firefighter Problem K Michalak Intelligent Data Engineering and Automated Learning–IDEAL 2018: 19th …, 2018 | 1 | 2018 |
| Deriving knowledge from local optima networks for evolutionary optimization in inventory routing problem P Lipinski, K Michalak Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 3 | 2019 |
| Does the default pecking order impact systemic risk? Evidence from Brazilian data M Alexandre, TC Silva, K Michalak, FA Rodrigues European Journal of Operational Research 309 (3), 1379-1391, 2023 | 5 | 2023 |
| Dynamic correlation approach to early stopping in artificial neural network training: macroeconomic forecasting example K Michalak, R Raciborski 5th International Conference on Intelligent Systems Design and Applications …, 2005 | 2 | 2005 |
| Dynamic Correlation Approach to Early Stopping in Neural Forecasting of Macroeconomic Indices K Michalak, R Raciborski Journal of Applied Computer Science 15 (2), 27-40, 2007 | | 2007 |
| ED-LS–A heuristic local search for the multiobjective Firefighter Problem K Michalak Applied Soft Computing 59, 389-404, 2017 | 13 | 2017 |
