Peter Korošec

Journal articlesshow all

Beyond Landscape Analysis: DynamoRep Features For Capturing Algorithm-Problem Interaction In Single-Objective Continuous Optimization Evolutionary Computation, ECJ (2025)
Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks, ECJ (2024)
Opt2Vec - A continuous optimization problem representation based on the algorithm: a case study on problem classification, INFORM SCIENCES (2024)

Conference papersshow all

Adaptive Estimation of the Number of Algorithm Runs in Stochastic Optimization, GECCO 2025
Per-Run Algorithm Performance Improvement Forecasting Using Exploratory Landscape Analysis Features: A Case Study in Single-Objective Black-Box Optimization, GECCO 2024

Books & Chaptersshow all

Reliability Improvements for In-Wheel Motor, Recent Advances in Microelectronics Reliability, Springer (2024)
Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms, Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms, Springer Cham (2022)

Other publicationsshow all

A GRASS GIS parallel module for radio-propagation predictions

L. Benedičič, F. A. Cruz, T. Hamada, P. Korošec
note: In Press

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BibTex:
@ARTICLE{BenCruHamKor14,
author = {Lucas Benediv{c}iv{c} and Filipe A. Cruz and Tsuyoshi Hamada and Peter Koro\v{s}ec},
journal = {International Journal of Geographical Information Science},
doi = {http://dx.doi.org/10.1080/13658816.2013.879151},
number = {x},
pages = {xx--xx},
title = {A GRASS GIS parallel module for radio-propagation predictions},
volume = {xx},
year = {2014}
}

abstract:
Geographical information systems are ideal candidates for the application of parallel programming techniques, mainly because they usually handle large data sets. To help us deal with complex calculations over such data sets, we investigated the performance constraints of a classic master–worker parallel paradigm over a message-passing communication model. To this end, we present a new approach that employs an external database in order to improve the calculation–communication overlap, thus reducing the idle times for the worker processes. The presented approach is implemented as part of a parallel radio-coverage prediction tool for the Geographic Resources Analysis Support System (GRASS) environment. The prediction calculation employs digital elevation models and land-usage data in order to analyze the radio coverage of a geographical area. We provide an extended analysis of the experimental results, which are based on real data from an Long Term Evolution (LTE) network currently deployed in Slovenia. Based on the results of the experiments, which were performed on a computer cluster, the new approach exhibits better scalability than the traditional master–worker approach. We successfully tackled real-world-sized data sets, while greatly reducing the processing time and saturating the hardware utilization.