Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Jankovic, Anja"'
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
Autor:
Kostovska, Ana, Jankovic, Anja, Vermetten, Diederick, Džeroski, Sašo, Eftimov, Tome, Doerr, Carola
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can help replace tedious and labor-intensive manual selection, and have a
Externí odkaz:
http://arxiv.org/abs/2306.17585
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly configurable with many different design choices regarding the init
Externí odkaz:
http://arxiv.org/abs/2306.04262
Autor:
Benjamins, Carolin, Jankovic, Anja, Raponi, Elena, van der Blom, Koen, Lindauer, Marius, Doerr, Carola
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices fo
Externí odkaz:
http://arxiv.org/abs/2211.09678
Autor:
Benjamins, Carolin, Raponi, Elena, Jankovic, Anja, van der Blom, Koen, Santoni, Maria Laura, Lindauer, Marius, Doerr, Carola
Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquisition function (AF), or the initial design, is subject to a wide range
Externí odkaz:
http://arxiv.org/abs/2211.01455
Autor:
Kostovska, Ana, Jankovic, Anja, Vermetten, Diederick, de Nobel, Jacob, Wang, Hao, Eftimov, Tome, Doerr, Carola
Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selection is classically done of
Externí odkaz:
http://arxiv.org/abs/2204.09483
Autor:
Jankovic, Anja, Vermetten, Diederick, Kostovska, Ana, de Nobel, Jacob, Eftimov, Tome, Doerr, Carola
Landscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization algorithms in the portfolio. This introduces a significant overhead i
Externí odkaz:
http://arxiv.org/abs/2204.06397
Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques. In the context of numerical optimization, supervised r
Externí odkaz:
http://arxiv.org/abs/2104.10999
Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a significantly growing number of applications, the underlying machine
Externí odkaz:
http://arxiv.org/abs/2104.09272
Black-box optimization is a very active area of research, with many new algorithms being developed every year. This variety is needed, on the one hand, since different algorithms are most suitable for different types of optimization problems. But the
Externí odkaz:
http://arxiv.org/abs/2102.05370