Zobrazeno 1 - 10
of 543
pro vyhledávání: '"Korošec, Peter"'
Autor:
Kostovska, Ana, Cenikj, Gjorgjina, Vermetten, Diederick, Jankovic, Anja, Nikolikj, Ana, Skvorc, Urban, Korosec, Peter, Doerr, Carola, Eftimov, Tome
The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires balancing the trade-off between the higher flexibility of large portfol
Externí odkaz:
http://arxiv.org/abs/2310.10685
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These features can be used as input for ML models that are trained to select or
Externí odkaz:
http://arxiv.org/abs/2306.05438
Autor:
Nikolikj, Ana, Džeroski, Sašo, Muñoz, Mario Andrés, Doerr, Carola, Korošec, Peter, Eftimov, Tome
In black-box optimization, it is essential to understand why an algorithm instance works on a set of problem instances while failing on others and provide explanations of its behavior. We propose a methodology for formulating an algorithm instance fo
Externí odkaz:
http://arxiv.org/abs/2306.00479
Autor:
Nikolikj, Ana, Cenikj, Gjorgjina, Ispirova, Gordana, Vermetten, Diederick, Lang, Ryan Dieter, Engelbrecht, Andries Petrus, Doerr, Carola, Korošec, Peter, Eftimov, Tome
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 model. The predictive model uses the feature representation of a set
Externí odkaz:
http://arxiv.org/abs/2306.00040
Leave-one-problem-out (LOPO) performance prediction requires machine learning (ML) models to extrapolate algorithms' performance from a set of training problems to a previously unseen problem. LOPO is a very challenging task even for state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2305.19375
Autor:
Cenikj, Gjorgjina, Lang, Ryan Dieter, Engelbrecht, Andries Petrus, Doerr, Carola, Korošec, Peter, Eftimov, Tome
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 this paper, we evaluate three heuristics for selecting diverse problem in
Externí odkaz:
http://arxiv.org/abs/2204.11527
Autor:
Kostovska, Ana, Vermetten, Diederick, Džeroski, Sašo, Doerr, Carola, Korošec, Peter, Eftimov, Tome
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 could solve the problem is hence desirable. Recent studies in single-
Externí odkaz:
http://arxiv.org/abs/2204.07431
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. For this purpose, recent studies learn a supervised machine learning (ML
Externí odkaz:
http://arxiv.org/abs/2203.11828
Autor:
Korošec, Peter, Eftimov, Tome
Publikováno v:
In Information Sciences October 2024 680
Autor:
Rupar, Nina, Šelb, Julij, Košnik, Mitja, Zidarn, Mihaela, Andrejević, Slađana, Čulav, Ljerka, Grivčeva‐Panovska, Vesna, Korošec, Peter, Rijavec, Matija
Publikováno v:
In Gene 15 August 2024 919