An algorithm for the selection problem

By R. Dromey. 1986

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Citations in the corpus

AutoML: A Survey of the State-of-the-Art (2019, background): “Based on the work of [144] published in 1976, K.”
Routing Networks and the Challenges of Modular and Compositional Computation (2019, background): “Interestingly, this is a specific instance of the “Algorithm Selection Problem” [Rice, 1976].”
Boosting as a Metaphor for Algorithm Design (2003, methodology): “When algorithms exhibit high runtime variance, one is faced with the problem of deciding which algorithm to use; in 1976 Rice dubbed this the “algorithm selecti on problem” [13].”
Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, background): “Algorithm selection originally dates back to 1970’s, where many researcher tried to design better algorithms for hard combinatorial problems [51], [146].”
Learning to Select Branching Rules in the DPLL Procedure for Satisfiability (2001, background): “Algorithm selection is the problem of selecting the most efficient algorithm among equivalent ones for a given problem instance (Rice, 1976).”
SATzilla: Portfolio-based Algorithm Selection for SAT (2008, background): “Thus, practitioners with hard SAT problems to solve face a potentially difficult “algorithm selection problem” (Rice, 1976): which algorithm(s) should be run in order to minimize some performance objective, such as expected runtime? The most widely-adopted solution to such algorithm selection proble”
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2017, methodology): “Adapted from (Rice, 1976; Smith-Miles, 2008). number of data points), and those that describe properties of the target protein (e.g. hydrophobicity).”
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021, methodology): “In the related problem of algorithm selection (Rice, 1976), we usually train a meta-learning model offline to select an optimal element from a finite set of algorithms or HPCs (Bischl et al., 2016).”
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2017, background): “The algorithm selection problem is an important problem in many domains and was first introduced in the 1970s (Rice 1976).”
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).”
Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract) (2015, methodology): “This classic problem of selecting the best from a given set of algorithms on a per-instance basis [Rice, 1976; Smith-Miles, 2009; Kotthoff, 2014] has been successfully addressed by using EPMs to predict the performance of all candidate algorithms and selecting the one predicted to perform best…”
Automated Algorithm Selection: Survey and Perspectives (2018, methodology): “This problem has already been considered in the seminal work by Rice (1976), but it took several decades before practical per-instance algorithm selection methods became available (see, e.g., Cook and Varnell, 1997; Leyton-Brown et al., 2003; Xu et al., 2008).”
Identifying Key Algorithm Parameters and Instance Features Using Forward Selection (2013, methodology): “EPMs for predicting the “empirical hardness” of instances have their origin over a decade ago [14, 15, 16, 17] and have been the preferred core reasoning tool of early state-of-the-art methods for the algorithm selection problem (which aim to select the best algorithm for a given problem, dependent ”
Algorithm runtime prediction: Methods & evaluation (2012, methodology): “This classic problem of selecting the best from a given set of algorithms on a per-instance basis [95,104] has been successfully addressed by using EPMs to predict the performance of all candidate algorithms and selecting the one predicted to perform best [18,79,26,45,97,119,70].”
Practical Deep Learning Architecture Optimization (2017, background): “Algorithm selection and hyperparameter optimization are a very old problem [23] and not limited to machine learning applications.”
Meta-Learning (2009, unknown): “This problem can be seen as an algorithm selection task (Rice 1976) [9].”