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

Multi-objective optimization benchmarking using DSCTool

P. Korošec, T. Eftimov
DOI: 10.3390/math8050839

BibTex:
@article{korovsec2020multi,
title={Multi-Objective Optimization Benchmarking Using DSCTool},
author={Koro{\v{s}}ec, Peter and Eftimov, Tome},
journal={Mathematics},
volume={8},
number={5},
pages={839},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}

abstract:
By performing data analysis, statistical approaches are highly welcome to explore the data. Nowadays with the increases in computational power and the availability of big data in different domains, it is not enough to perform exploratory data analysis (descriptive statistics) to obtain some prior insights from the data, but it is a requirement to apply higher-level statistics that also require much greater knowledge from the user to properly apply them. One research area where proper usage of statistics is important is multi-objective optimization, where the performance of a newly developed algorithm should be compared with the performances of state-of-the-art algorithms. In multi-objective optimization, we are dealing with two or more usually conflicting objectives, which result in high dimensional data that needs to be analyzed. In this paper, we present a web-service-based e-Learning tool called DSCTool that can be used for performing a proper statistical analysis for multi-objective optimization. The tool does not require any special statistics knowledge from the user. Its usage and the influence of a proper statistical analysis is shown using data taken from a benchmarking study performed at the 2018 IEEE CEC (The IEEE Congress on Evolutionary Computation) is appropriate. Competition on Evolutionary Many-Objective Optimization.