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3. Instance selection for dynamic algorithm configuration with reinforcement learning : improving generalizationCarolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer, 2024, objavljeni znanstveni prispevek na konferenci Ključne besede: dynamic algorithm configuration, reinforcement learning, instance selection, generalization Objavljeno v DiRROS: 16.09.2024; Ogledov: 752; Prenosov: 206 Celotno besedilo (767,63 KB) Gradivo ima več datotek! Več... |
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5. Generalization ability of feature-based performance prediction models : a statistical analysis across benchmarksAna Nikolikj, Ana Kostovska, Gjorgjina Cenikj, Carola Doerr, Tome Eftimov, 2024, objavljeni znanstveni prispevek na konferenci Povzetek: This study examines the generalization ability of algorithm performance prediction models across various bench-mark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between these two measures. Specifically, when the high-dimensional feature value distributions between training and testing suites lack statistical significance, the model tends to generalize well, in the sense that the testing errors are in the same range as the training errors. Two experiments validate these findings: one involving the standard benchmark suites, the BBOB and CEC collections, and another using five collections of affine combinations of BBOB problem instances. Ključne besede: meta-learning, single-objective optimization, module importance Objavljeno v DiRROS: 16.09.2024; Ogledov: 190; Prenosov: 87 Celotno besedilo (1,29 MB) Gradivo ima več datotek! Več... |
6. A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimizationGjorgjina Cenikj, Gašper Petelin, Tome Eftimov, 2024, izvirni znanstveni članek Povzetek: The task of selecting the best optimization algorithm for a particular problem is known as algorithm selection (AS). This involves training a model using landscape characteristics to predict algorithm performance, but a key challenge remains: making AS models generalize effectively to new, untrained benchmark suites. This study assesses AS models’ generalizability in single-objective numerical optimization across diverse benchmark suites. Using Exploratory Landscape Analysis (ELA) and transformer-based (TransOpt) features, the research investigates their individual and combined effectiveness in AS across four benchmarks: BBOB, AFFINE, RANDOM, and ZIGZAG. AS models perform differently based on benchmark suite similarities in algorithm performance distributions and single-best solvers. When suites align, these models underperform against a baseline predicting mean algorithm performance; yet, they outperform the baseline when suites differ in performance distributions and solvers. The AS models trained using the ELA landscape features are better than the models trained using the TransOpt features on the BBOB and AFFINE benchmark suites, while the opposite is true for the RANDOM benchmark suite. Ultimately, the study reveals challenges in accurately capturing algorithm performance through problem landscape features (ELA or TransOpt), impacting AS model applicability. Ključne besede: algorithm selection, multi-target regression, generalization, benchmarking Objavljeno v DiRROS: 21.05.2024; Ogledov: 439; Prenosov: 480 Celotno besedilo (2,49 MB) Gradivo ima več datotek! Več... |
7. PS-AAS : portfolio selection for automated algorithm selection in black-box optimizationAna Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Janković, Ana Nikolikj, Urban Škvorc, Peter Korošec, Carola Doerr, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: automated algorithm selection, portfolio selection, black box optimization Objavljeno v DiRROS: 11.12.2023; Ogledov: 750; Prenosov: 300 Celotno besedilo (1,90 MB) Gradivo ima več datotek! Več... |
8. Assessing the generalizability of a performance predictive modelAna Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: algorithms, predictive models, machine learning Objavljeno v DiRROS: 15.09.2023; Ogledov: 718; Prenosov: 429 Celotno besedilo (935,67 KB) Gradivo ima več datotek! Več... |
9. DynamoRep : trajectory-based population dynamics for classification of black-box optimization problemsGjorgjina Cenikj, Gašper Petelin, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: black-box single-objective optimization, optimization problem classification, problem representation, meta-learning Objavljeno v DiRROS: 30.08.2023; Ogledov: 689; Prenosov: 431 Celotno besedilo (650,13 KB) Gradivo ima več datotek! Več... |
10. FooDis : a food-disease relation mining pipelineGjorgjina Cenikj, Tome Eftimov, Barbara Koroušić-Seljak, 2023, izvirni znanstveni članek Povzetek: Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx. Ključne besede: text mining, relation extraction, named entity recognition, named entity linking, food-disease relations Objavljeno v DiRROS: 25.05.2023; Ogledov: 649; Prenosov: 374 Celotno besedilo (1,11 MB) Gradivo ima več datotek! Več... |