Algorithm portfolios

By Carla P. Gomes, Bart Selman. Artificial Intelligence, 2001

506 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3686cc6a1b864b8912d6cf22d3af673709d99627

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In Computational Theory and Mathematics

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In Management Science and Operations Research

Citations in the corpus

Automated configuration of algorithms for solving hard computational problems (2009, methodology): “This suggests splitting such heterogeneous sets of instances into more homogeneous subsets, using portfolio techniques (Gomes and Selman, 2001; Horvitz et al., 2001; Xu et al., 2008), or using per-instance algorithm configuration (Hutter et al.”
Automated Algorithm Selection: Survey and Perspectives (2018, background): “guration. One prominent approach is that of a parallel algorithm portfolio, where each algorithm from a given set A is run in parallel on a given problem instance i (see, e.g., Huberman et al., 1997; Gomes and Selman, 2001; Fukunaga, 2000). When applied to a decision problem, all runs are terminated”
Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection (2010, unknown): “and Schaerf 2007; Monette, Deville, and van Hentenryck 2009) as well as parallel portfolios and online selection (Huberman, Lukose, and Hogg 1997; Gomes and Selman 2001; Carchrae and Beck 2005; Gagliolo and Schmidhuber 2006; Streeter, Golovin, and Smith 2007).”
ISAC - Instance-Specific Algorithm Configuration (2010, background): “[11] proposed to run in parallel (or interleaved on a single processor) multiple stochastic solvers that tackle the same problem.”
Boosting as a Metaphor for Algorithm Design (2003, methodology): “Gomes and Selman [3] proposed such an approach for incomplete SAT algo rithms, using the term portfolio to describe a set of algorithms run in parallel.”
SATzilla: Portfolio-based Algorithm Selection for SAT (2008, background): “This approach was also studied by Gomes and Selman (2001).”
Instance-Specific Algorithm Configuration (2014, background): “Even though no individual solver can do better, when they join forces in a portfolio performance can be significantly boosted, as the pioneers of this research thread have pointed out [35, 58].”
A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms (2009, background): “Tuning problems with small sets of parameter configurations were considered in [2], a setting which is closely related to that in algorithm portfolios [6,7].”
Optimally Robust Private Information Retrieval (2012, methodology): “Our implementation assembles the error correction algorithms described in Section 2.3 into a portfolio algorithm [20] to do efficient decoding.”
Planning as satisfiability: Heuristics (2012, methodology): “We consider translations of hard combinatorial search problems into planning as proposed by Porco et al. [10], as well as hard instances generated according to problem parameters that have been empirically determined to be hard for existing algorithms [11].”
An Online Algorithm for Maximizing Submodular Functions (2008, background): “Previous work has shown that combining multiple heuristics for NP-hard problems into a portfolio can dramatically reduce average-case running time [8, 9, 19].”
Algorithm Selection for Combinatorial Search Problems: A Survey (2012, methodology): “Gomes and Selman (1997a, 2001) use the runtime distributions of algorithms over the size of a problem, as measured by the number of backtracks.”
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020, background): “…time shares to multiple algorithms instead of allocating all available time to a single one reduces the average cost for solving a problem (Huberman et al., 1997; Gomes and Selman, 2001), and had applications in different sub-fields of AI (Smith-Miles, 2008; Kotthoff, 2014; Kerschke et al., 2019).”
Alors: An algorithm recommender system (2017, methodology): “In this domain, extensive algorithm portfolios have been proposed following Gomes and Selman (2001) and signiﬁcant advances regarding algorithm selection have been made in the last decade (see Kotthoﬀ (2014) for a survey; section 2).”