Journal ArticleDOI
An algorithm for the selection problem
TLDR
A refinement to a well‐known selection algorithm is described, which results in a useful improvement in the performance of the original algorithm, particularly when the selection index is small relative to the median.Abstract:
A refinement to a well-known selection algorithm is described. The refinement results in a useful improvement in the performance of the original algorithm, particularly when the selection index is small relative to the median.read more
Citations
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Journal ArticleDOI
Hyper-heuristics: a survey of the state of the art
Edmund K. Burke,Michel Gendreau,Matthew Hyde,Graham Kendall,Gabriela Ochoa,Ender Özcan,Rong Qu +6 more
TL;DR: A critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas are presented.
Journal ArticleDOI
SATzilla: portfolio-based algorithm selection for SAT
TL;DR: SATzilla is described, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers and is improved by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances.
Journal ArticleDOI
AutoML: A survey of the state-of-the-art
Xin He,Kaiyong Zhao,Xiaowen Chu +2 more
TL;DR: A comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML methods according to the pipeline, covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS).
Book
Linear Programming 1: Introduction
TL;DR: Encompassing all the major topics students will encounter in courses on the subject, the authors teach both the underlying mathematical foundations and how these ideas are implemented in practice, making this an ideal textbook for all those coming to the subject for the first time.
Journal ArticleDOI
Algorithm runtime prediction: Methods & evaluation
TL;DR: In this paper, the authors describe extensions and improvements of existing models, new families of models, and a much more thorough treatment of algorithm parameters as model inputs, and comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP), and mixed integer programming (MIP) problems.
References
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Journal ArticleDOI
Expected time bounds for selection
Robert W. Floyd,Ronald L. Rivest +1 more
TL;DR: A new selection algorithm is presented which is shown to be very efficient on the average, both theoretically and practically.
Journal ArticleDOI
Proof of a program: FIND
TL;DR: A proof is given of the correctness of the algorithm “Find” and some conclusions relating to general programming methodology are drawn.
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