A portfolio approach to algorithm select

By Hendrik Zender, Geert-Jan M. Kruijff, Ivana Kruijff-Korbayová. 2003

167 citations recorded by the source corpus.

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Citations in the corpus

Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, background): “Another approach is to model the ASP as a regression problem, in which one tries to predict the performance of each algorithm for a given instance and the best one is selected (Xu et al., 2008; Leyton-Brown et al., 2003b; Battistutta et al., 2017).”
Alors: An algorithm recommender system (2017, methodology): “This selection is known under various names depending on the ﬁeld: algorithm selection and conﬁguration in SAT and CSP (Rice, 1976; Leyton-Brown et al., 2003; Epstein et al., 2006; Samulowitz and Memisevic, 2007; Xu et al., 2008),(O´Mahony et al., 2008; Hutter et al., 2009; Pulina and Tacchella,…”
SATzilla: Portfolio-based Algorithm Selection for SAT (2008, methodology): “Eugene Nudelman, Alex Devkar and Yoav Shoham were Kevin and Holger’s co-authors on the papers that first introduced SATzilla (Nudelman et al., 2004a, 2004b); this work grew out of a project on automated algorithm selection that involved Galen Andrew and Jim McFadden, in addition to Kevin, Eugene and”
Automated Algorithm Configuration and Parameter Tuning (2012, methodology): “…to be applied to a given problem instance based on properties of that instance determined just before attempting to solve it (see, e.g., Rice, 1976; Leyton-Brown et al, 2003; Guerri and Milano, 2004; Xu et al, 2007, 2008); similarly, per-instance algorithm configuration methods use instance…”
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].”
Cross-disciplinary perspectives on meta-learning for algorithm selection (2009, methodology): “Throughout a series of papers over several years, Leyton-Brown and co-authors extended the methodology to consider algorithm selection in algorithm portfolios [Leyton-Brown et al. 2003a], and turned their attention to the SAT problem [Nudelman et al. 2004].”
Hydra-MIP : Automated Algorithm Configuration and Selection for Mixed Integer Programming (2011, methodology): “This algorithm selection problem [24] can be solved by gathering cheaply computable features from the problem instance and then evaluating a learned model to select the best algorithm [20, 9, 6].”
Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection (2010, unknown): “A second influential technique is portfolio-based algorithm selection (Rice 1976; Leyton-Brown et al. 2003b; Guerri and Milano 2004; Leyton-Brown, Nudelman, and Shoham 2009).”
Boosting as a Metaphor for Algorithm Design (2003, methodology): “1, reproduced from [6]).”
Algorithm runtime prediction: Methods & evaluation (2012, methodology): “we developed techniques for identifying sets of features that correlate strongly with runtime. However, we quickly found that these models were also useful as engineering tools, in at least four ways [66, 67, 79, 46]. First, EHMs have been the key technology behind the ﬁrst algorithm selection appro”