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)

The Parameter-Less Evolutionary Search for Real-Parameter Single Objective Optimization

G. Papa, J. Šilc
Proc. 2013 IEEE Congress on Evolutionary Computation
Cancun, Mexico: 20-23 June 2013
pages: 1131-1137

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BibTex:
@inproceedings{PapaSilc_CEC2013,
author = {Papa, Gregor and Silc, Jurij},
title = {The Parameter-Less Evolutionary Search for Real-Parameter Single Objective Optimization},
booktitle = {2013 IEEE Conference on Evolutionary Computation},
volume = 1,
year = 2013,
month = {June 20-23},
pages = {1131--1137}
}

abstract:
A parameter-less algorithm allows optimal solutions to be found without the need for setting the control parameters. Namely, finding an appropriate parameter setup for an evolutionary algorithm is a challenging research problem, and the setup optimality is crucial for algorithm's good performance. Therefore, the approaches that are able to solve any problem without any human intervention to set suitable control parameters are particulary interesting. The Parameter-less Evolutionary Search (PLES) algorithm, with its real-value and combinatorial version, is based on a basic genetic algorithm, but it does not need any control parameter to be set in advance. It is able to find optimal, or at least very good, solutions relatively quickly, and without the need for a parameter-setting specialist. The last of these is a very important issue when used by engineers that do not have a detailed background knowledge: neither about optimization algorithms, nor about the settings of their control parameters. The efficiency of the proposed parameter-less algorithm was already evaluated using theoretical and real-world problems, being either real-valued or combinatorial. It was shown that the presented, adaptive, parameter-less algorithm has a faster convergence than comparable algorithms. Furthermore, it demonstrates its search ability by finding the solution without the need for predefined control parameters.