21 citations · 54 across the 10 of their papers we have counts for
10 papers
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
Gjorgjina Cenikj, Gašper Petelin, Carola Doerr +2
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These feat…
Assessing the Generalizability of a Performance Predictive Model
Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova +6
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive…
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison
Gjorgjina Cenikj, Ryan Dieter Lang, Andries Petrus Engelbrecht +3
Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets that are non-redundant and are representative of typical optimization scenarios. In thi…
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
Ana Kostovska, Diederick Vermetten, Sašo Džeroski +3
Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm…
Explainable Landscape Analysis in Automated Algorithm Performance Prediction
Risto Trajanov, Stefan Dimeski, Martin Popovski +2
Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance. Fo…
Explainable Landscape-Aware Optimization Performance Prediction
Risto Trajanov, Stefan Dimeski, Martin Popovski +2
Efficient solving of an unseen optimization problem is related to appropriate selection of an optimization algorithm and its hyper-parameters. For this purpose, automated algorithm…
A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems
Urban Škvorc, Tome Eftimov, Peter Korošec
When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems. One possible way o…
Personalizing Performance Regression Models to Black-Box Optimization Problems
Tome Eftimov, Anja Jankovic, Gorjan Popovski +2
Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configurat…
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes
Tome Eftimov, Gorjan Popovski, Quentin Renau +2
Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning,…
A GRASS GIS parallel module for radio-propagation predictions
Lucas Benedičič, Felipe A. Cruz, Tsuyoshi Hamada +1
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 wi…