Gregor Papa

Orcid

Journal articlesshow all

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

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
An evolutionary approach to pessimistic bilevel optimization problems, BILEVEL 23
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)

Applications of Dynamic Parameter Control in Evolutionary Computation -tutorial

G. Papa
Proc. Proc. Genetic and Evolutionary Computation Conference Companion
Lille, France: 10-14 July 2021
DOI: 10.1145/3449726.3461435

BibTex:
inbook{10.1145/3449726.3461435,
author = {Papa, Gregor},
title = {Applications of Dynamic Parameter Control in Evolutionary Computation},
year = {2021},
isbn = {9781450383516},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3449726.3461435},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {1064–1088},
numpages = {25}
}

abstract:
One of the challenging problems in solving optimization problems with evolutionary algorithms is the selection of the control parameters, which allow to adjust the behaviour of the algorithms to the problem at hand. Several control parameters need to be set, for the procedure of searching for the optimum of an objective function to be successful. Suitable control parameter values need to be found, for example, for the population size, the mutation strength, the crossover rate, the selective pressure, etc. The choice of these parameters can have a significant impact on the performance of the algorithm and need thus to be executed with care. With parameter control approach no prior training of parameters is needed. It also accounts for the fact that the optimal parameter values typically change during the optimization process: for example, at the beginning of an optimization process we typically aim for exploration, while in the later stages we want the algorithm to converge and to focus its search on the most promising regions in the search space. While parameter control is indispensable in continuous optimization, it is far from being well-established in discrete optimization heuristics. The ambition of this tutorial is to inform participants about different parameter control techniques, and by discussing both theoretical as well as experimental results that demonstrate the unexploited potential of non-static parameter choices. Our tutorial addresses experimentally and theory-oriented researchers alike, and requires only basic knowledge of optimization heuristics.