SIS PUT | dr hab. inż. Miłosz Kadziński

1.

From investigation of expressiveness and robustness to a comprehensive value-based framework for multiple criteria sorting problems / Miłosz Kadziński (WIiT), Michał Wójcik (WIiT), Mohammad Ghaderi // Omega - 2025, vol. 131, p. 103203-1-103203-25

Scientific article 140 6,7
2.

A multiple criteria Bayesian hierarchical model for analyzing heterogeneous consumer preferences / Jiapeng Liu, Yan Wang, Miłosz Kadziński (WIiT), Xiaoxin Mao, Yuan Rao // Omega - 2024, vol. 128, p. 103113-1-103113-19

Scientific article 140 6,7
3.

Deep dive into RNA: a systematic literature review on RNA structure prediction using machine learning methods / Michał Budnik (WIiT,WIiT), Jakub Wawrzyniak, Łukasz Grala, Miłosz Kadziński (WIiT), Natalia Szóstak // Artificial Intelligence Review - 2024, vol. 57, iss. 9, p. 254-1-254-41

Scientific article 140 10,7
4.

Outranking-based approaches for multiple criteria partially ordered clustering: A review of existing algorithms, new proposals, and experimental comparison / Dariusz Grynia (WIiT), Klaudia Dobrogojska (WIiT), Miłosz Kadziński (WIiT) // Information Sciences - 2024, vol. 678, p. 121014-1-121014-26

Scientific article 200
5.

robustDEA: A Java library for robust efficiency analysis / Anna Labijak-Kowalska (WIiT), Miłosz Kadziński (WIiT) // SoftwareX - 2024, vol. 26, p. 101749-1-101749-8

Scientific article 200 2,4

All ARTICLES

1.

Interactive tool for visualizing the comprehensive performance of evolutionary multi-objective algorithms applied to problems with two or three objectives / Michał K. Tomczyk (WIiT), Miłosz Kadziński (WIiT) // W: GECCO '24 Companion : Proceedings of the Genetic and Evolutionary Computation Conference Companion, Melbourne, VIC, Australia, July 14 - 18, 2024 / red. Xiaodong Li - New York, United States : Association for Computing Machinery (ACM), 2024 - p. 375-378

Chapter in monograph / paper 20 140
2.

Nature-inspired Preference Learning Algorithms Using the Choquet Integral / Michał Wójcik (WIiT), Miłosz Kadziński (WIiT) // W: GECCO '24 : Proceedings of the 2024 Genetic and Evolutionary Computation Conference, July 14-18, 2024, Melbourne, Australia - New York, USA : Association for Computing Machinery (ACM), 2024 - p. 440-448

Chapter in monograph / paper 20 140
3.

Co-evolution improves the efficiency of preference learning methods when the Decision Maker's aspirations develop over time / Michał K. Tomczyk (WIiT), Miłosz Kadziński (WIiT) // W: GECCO '23 : Proceedings of the Genetic and Evolutionary Computation Conference, July 15-19, 2023, Lisbon, Portugal - New York, United States : Association for Computing Machinery (ACM), 2023 - p. 759-767

Chapter in monograph / paper 20 140
4.

Metaphor-based algorithms for learning the preference model parameters of FlowSort from large sets of assignment examples / Adam Handke (WIiT), Damian Jóźwiak (WIiT), Piotr Kamiński (WIiT), Michał Wiśniewski (WIiT), Miłosz Kadziński (WIiT) // W: GECCO '23 Companion : Proceedings of the Companion Conference on Genetic and Evolutionary Computation, July 15-19, 2023, Lisbon, Portugal - New York, United States : Association for Computing Machinery (ACM), 2023 - p. 283-286

Chapter in monograph / paper 20 140
5.

Aggregation of Stochastic Rankings in Group Decision Making / Miłosz Kadziński (WIiT), Grzegorz Miebs (WIiT), Dariusz Grynia (WIiT), Roman Słowiński // W: Collective Decisions: Theory, Algorithms And Decision Support Systems / red. Tomasz Szapiro, Janusz Kacprzyk - Cham, Switzerland : Springer, 2022 - p. 83-101

Chapter in monograph 20

All CHAPTERS

1.

On the elicitation of indirect preferences in interactive evolutionary multiple objective optimization / Michał Tomczyk (WIiT), Miłosz Kadziński (WIiT) // r888_2020

Research report
2.

Decomposition-based co-evolutionary algorithm for interactive multiple objective optimization driven by pairwise comparisons. / Michał K. Tomczyk (WI), Miłosz Kadziński (WI) // r588_2019

Research report
3.

Compariosn of ranking for comprehensive resilience assessment of electricity supply security / Miłosz Kadziński (WI) // r261_2019

Research report
4.

Robust indicator-based algorithm for interactive evolutionary multiple objective optimization / Michał K. Tomczyk (WI), Miłosz Kadziński (WI) // r557093_2019

Research report
5.

Quantifying electricity supply resilience of 140 countries using robust efficiency analysis / Anna Labijak (WI), Miłosz Kadziński (WI) // r545491_2018

Research report

All REPORTS

1.

New Directions in Robustness Analysis and Preference Modeling in Multiple Criteria Decision Aiding / Miłosz Kadziński (WI) / prom. Roman Słowiński (WI), rev. Włodzimierz Ogryczak, Tadeusz Trzaskalik - Poznań, Polska, 2012 - 248 s.

Dissertation

1.

Computer-aided decision support methods resistant to imperfections of learning data / Grzegorz Miebs (WIiT) / prom. Miłosz Kadziński (WIiT), rev. Jarosław Wątróbski, Konrad Kułakowski, Bogumił Kamiński - Poznań, Polska, 2024 - 278 s.

Dissertation
2.

New Directions in Robust Data Envelopment Analysis / Anna Labijak-Kowalska (WIiT) / prom. Miłosz Kadziński (WIiT), rev. Elżbieta Kubińska, Konrad Kułakowski, Kesra Nermend - Poznań, Polska, 2023 - 322 s.

Dissertation
3.

Multiple Criteria Decision Analysis methods inspired by other sub-disciplines of artificial intelligence / Krzysztof Martyn (WIiT) / prom. Miłosz Kadziński (WIiT), rev. Ewa Roszkowska, Tomasz Wachowicz, Paweł Ziemba - Poznań, Polska, 2023 - 244 s.

Dissertation
4.

New Directions in Interactive Evolutionary Multiple Objective Optimization Based on Holistic Preference Information / Michał Tomczyk (WIiT) / prom. Miłosz Kadziński (WIiT), rev. Konrad Kułakowski, Bogumił Kamiński - Poznań, Polska, 2020 - 306 s.

Dissertation
5.

Advances in Preference Disaggregation and Post Factum Analysis with Multiple Criteria Additive Value Models / Krzysztof Jan Ciomek (WI) / prom. Miłosz Kadziński (WI), rev. Konrad Kułakowski, Tomasz Wachowicz - Poznań, Polska, 2020 - 244 s.

Dissertation