Algorithm Selector and Prescheduler in the ICON Challenge

Gonard, François; Schoenauer, Marc; Sebag, Michèle

doi:10.1007/978-3-319-95104-1_13

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…This is not surprising since sunny-as2 is quite similar to sunny-as2-fk-OASC. ASAP.v2 does not attain the best score in any scenario, but in general its performance is robust and effectivethis confirms what reported in (Gonard et al, 2019). AutoFolio is slightly behind ASAP.v2, nevertheless it achieves good results and it is the best approach for the Magnus scenario.…”

Section: Comparison With Other Approachessupporting

confidence: 79%

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sunny-as2: Enhancing SUNNY for Algorithm Selection

Liu¹,

Amadini

Gabbrielli

et al. 2021

jair

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SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as, a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as, called sunny-as2, was then submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work we present the technical advancements of sunny-as2, by detailing through several empirical evaluations and by providing new insights. Its current version, built on the top of the preliminary version submitted to OASC, is able to outperform sunny-as and other state-of-the-art AS methods, including those who did not attend the challenge.

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Section: Comparison With Other Approachessupporting

confidence: 79%

“…As by Gonard et al (2019Gonard et al ( , 2017, the relevant parameters for ASAP-v2 are the number of estimators (decision trees) and the weight for regularization. In Tab.…”

Section: Appendix a Composition Of Oasc Scenariosmentioning

confidence: 99%

sunny-as2: Enhancing SUNNY for Algorithm Selection

Liu¹,

Amadini

Gabbrielli

et al. 2021

jair

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show abstract

“…Most importantly, we cannot properly build and evaluate meta-solvers that schedule more than one individual solver in the solving time window [0, τ ), because we do not know the best solution found by a solver at a time point t < τ . This is unfortunate since scheduling different solvers is very common for a number of effective meta-solvers (Hula, Mojzísek, & Janota, 2021;Gonard, Schoenauer, & Sebag, 2017;H. Hoos, Kaminski, Lindauer, & Schaub, 2015;Amadini, Gabbrielli, & Mauro, 2014;Malitsky, Sabharwal, Samulowitz, & Sellmann, 2013;Xu, Hutter, Hoos, & Leyton-Brown, 2008).…”

Section: Optimization Problemsmentioning

confidence: 99%

On the Evaluation of (Meta-)solver Approaches

Amadini¹,

Gabbrielli²,

Liu³

et al. 2023

jair

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Meta-solver approaches exploit many individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can adopt the metrics typically used for individual solvers (e.g., runtime or solution quality) or employ more specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual best performance). In this paper, based on some recently published works, we provide an overview of different performance metrics for evaluating (meta-)solvers by exposing their strengths and weaknesses.

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“…• Gonard et al (2017) submitted ASAP.v2 and ASAP.v3 (Gonard et al, 2016). ASAP combines pre-solving schedules and per-instance algorithm selection by training both jointly.…”

Section: Appendix C Submitted Systems In 2017mentioning

confidence: 99%

“…• ASAP based on random forests (RF) and k-nearest neighbor (kNN) as selection models combine pre-solving schedule and per-instance algorithm selection by training both jointly (Gonard et al, 2016).…”

Section: Appendix B Technical Evaluation Details In 2015mentioning

confidence: 99%

The algorithm selection competitions 2015 and 2017

Lindauer

Rijn

Kotthoff

2019

Artificial Intelligence

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The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.

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Algorithm Selector and Prescheduler in the ICON Challenge

Cited by 6 publications

References 18 publications

sunny-as2: Enhancing SUNNY for Algorithm Selection

sunny-as2: Enhancing SUNNY for Algorithm Selection

On the Evaluation of (Meta-)solver Approaches

The algorithm selection competitions 2015 and 2017

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