Zobrazeno 1 - 10
of 1 760
pro vyhledávání: '"A. Eftimov"'
Autor:
Djukanović, Marko, Reixach, Jaume, Nikolikj, Ana, Eftimov, Tome, Kartelj, Aleksandar, Blum, Christian
This paper addresses the Restricted Longest Common Subsequence (RLCS) problem, an extension of the well-known Longest Common Subsequence (LCS) problem. This problem has significant applications in bioinformatics, particularly for identifying similari
Externí odkaz:
http://arxiv.org/abs/2410.12031
Autor:
Benjamins, Carolin, Cenikj, Gjorgjina, Nikolikj, Ana, Mohan, Aditya, Eftimov, Tome, Lindauer, Marius
Publikováno v:
GECCO 2024
Dynamic Algorithm Configuration (DAC) addresses the challenge of dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on individual tasks. Agents trained with Deep Reinforcement Learning (RL)
Externí odkaz:
http://arxiv.org/abs/2407.13513
Autor:
Cenikj, Gjorgjina, Nikolikj, Ana, Petelin, Gašper, van Stein, Niki, Doerr, Carola, Eftimov, Tome
The selection of the most appropriate algorithm to solve a given problem instance, known as algorithm selection, is driven by the potential to capitalize on the complementary performance of different algorithms across sets of problem instances. Howev
Externí odkaz:
http://arxiv.org/abs/2406.06629
This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that ar
Externí odkaz:
http://arxiv.org/abs/2405.12259
This study explores the influence of modules on the performance of modular optimization frameworks for continuous single-objective black-box optimization. There is an extensive variety of modules to choose from when designing algorithm variants, howe
Externí odkaz:
http://arxiv.org/abs/2405.11964
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
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