Gregor Papa

Orcid

Journal articlesshow all

Dealing with zero-inflated data: Achieving state-of-the-art with a two-fold machine learning approach, EAAI (2025)
Advanced computing to support urban climate neutrality, Energy Sustain. Soc. (2025)
δ-perturbation of bilevel optimization problems: An error bound analysis, Oper. Res. Perspect. (2024)
Models for forecasting the traffic flow within the city of Ljubljana, ETRE (2023)

Conference papersshow all

A Study on Optimistic and Pessimistic Pareto-Fronts in Multiobjective Bilevel Optimization via δ-Perturbation, EMO 2025
Traffic Forecasting With Uncertainty: A Case for Conformalized Quantile Regression, ECCOMAS 2024
Fleet and Traffic Management Systems for Conducting Future Cooperative Mobility, ECCOMAS 2024
Fleet and traffic management systems for conducting future cooperative mobility, TRA 2024
Users’ cognitive processes in a user interface for predicting football match results, IS 2023
An evolutionary approach to pessimistic bilevel optimization problems, BILEVEL 2023

Books & Chaptersshow all

A Framework for Applying Data-Driven AI/ML Models in Reliability, Recent Advances in Microelectronics Reliability, Springer (2024)
Reliability Improvements for In-Wheel Motor, Recent Advances in Microelectronics Reliability, Springer (2024)

Differential Evolution with Estimation of Distribution for Worst-case Scenario Optimization

M. Antoniou, G. Papa
Mathematics, 2021, Vol. 9, No. 17, pages: 22
DOI: 10.3390/math9172137

BibTex:
@Article{AntoniouPapa2021,
AUTHOR = {Antoniou, Margarita and Papa, Gregor},
TITLE = {Differential Evolution with Estimation of Distribution for Worst-Case Scenario Optimization},
JOURNAL = {Mathematics},
VOLUME = {9},
YEAR = {2021},
NUMBER = {17},
ARTICLE-NUMBER = {2137},
URL = {https://www.mdpi.com/2227-7390/9/17/2137},
ISSN = {2227-7390},
DOI = {10.3390/math9172137}
}

abstract:
Worst-case scenario optimization deals with the minimization of the maximum output in all scenarios of a problem, and it is usually formulated as a min-max problem. Employing nested evolutionary algorithms to solve the problem requires numerous function evaluations. This work proposes a differential evolution with an estimation of distribution algorithm. The algorithm has a nested form, where a differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, we estimate the distribution of the best worst solution for the best solutions found so far. The probabilistic model is used to sample part of the initial population of the scenario space differential evolution, using a priori knowledge of the previous generations. The method is compared with a state-of-the-art algorithm on both benchmark problems and an engineering application, and the related results are reported.