A Learning-Based Algorithm Selection Meta-Reasoner for the Real-Time MPE Problem

Guo, Haipeng; Hsu, William H.

doi:10.1007/978-3-540-30549-1_28

Cited by 11 publications

(12 citation statements)

References 11 publications

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“…Specifically, features 15-17 are based on the tour length obtained by LK; features 18-20, 21-23, and 24-26 are based on the tour length of local minima, the tour quality improvement per search step, and the number of search steps to reach a local minimum, respectively; features 27-29 measure the Hamming distance between two local minima; and features 30-32 describe the probability of edges appearing in any local minimum encountered during probing. Our branch and cut probing features(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43) are based on 2-second runs of Concorde. Specifically, features 33-35 measure the improvement of lower bound per cut; feature 36 is the ratio of upper and lower bound at the end of the probing run; and features 37-43 analyze the final LP solution.…”

mentioning

confidence: 99%

Algorithm runtime prediction: Methods & evaluation

Hutter

Hoos

et al. 2014

Artificial Intelligence

358

328

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Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio-based algorithm selection, and the automatic configuration of parameterized algorithms. Over the past decade, a wide variety of techniques have been studied for building such models. Here, we describe extensions and improvements of existing models, new families of models, andperhaps most importantly-a much more thorough treatment of algorithm parameters as model inputs. We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate these innovations through the largest empirical analysis of its kind, comparing to a wide range of runtime modelling techniques from the literature. Our experiments consider 11 algorithms and 35 instance distributions; they also span a very wide range of SAT, MIP, and TSP instances, with the least structured having been generated uniformly at random and the most structured having emerged from real industrial applications. Overall, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously. 102 21 59 109 6E3 1E3 1E3 6E3 Minisat 2.0-HAND 1903 3600 1.25 0.57 0.53 0.52 0.51 74 14 48 79 3E3 96 179 3E3 Minisat 2.0-RAND 2497 3600 0.82 0.39 0.38 0.37 0.37 59 23 52 65 221 35 145 230 Minisat 2.0-INDU 1146 3600 0.94 0.58 0.57 0.55 0.52 222 24 85 231 6E3 1E3 1E3 6E3 Minisat 2.0-SWV-IBM 466 3600 0.85 0.16 0.17 0.17 0.17 98 8.4 59 102 1E3 74 153 1E3 Minisat 2.0-IBM 834 3600 1.1 0.21 0.25 0.21 0.19 130 11 78 136 1E3 74 153 1E3 Minisat 2.0-SWV 0.89 5.32 0.25 0.08 0.09 0.08 0.08 57 4.9 34 59 217 17 123 226 CryptoMinisat-INDU 1921 3600 1.1 0.81 0.73 0.74 0.72 222 24 85 231 6E3 1E3 1E3 6E3 CryptoMinisat-SWV-IBM 873 3600 1.07 0.47 0.5 0.49 0.48 98 8.4 59 102 1081 74 153 1103 CryptoMinisat-IBM 1178 3600 1.2 0.42 0.45 0.42 0.41 130 11 78 136 1081 74 153 1103 CryptoMinisat-SWV 486 3600 0.89 0.51 0.53 0.49 0.51 57 4.9 34 59 217 17 123 226 SPEAR-INDU 1685 3600 1.01 0.67 0.62 0.61 0.58 222 24 85 231 6E3 1E3 1E3 6E3 SPEAR-SWV-IBM 587 3600 0.97 0.38 0.39 0.39 0.38 98 8.4 59 102 1E3 74 153 1E3 SPEAR-IBM 1004 3600 1.18 0.39 0.42 0.42 0.38 130 11 78 136 1E3 74 153 1E3 SPEAR-SWV 60 3600 0.54 0.36 0.34 0.34 0.34 57 4.9 34 59 217 17 123 226 tnm-RANDSAT 568 3600 1.05 0.88 0.97 0.9 0.88 63 26 56 70 221 35 145 230 SAPS-RANDSAT 1019 3600 1 0.67 0.71 0.65 0.66 63 26 56 70 221 35 145 230 CPLEX-BIGMIX 719 3600 0.96 0.84 0.85 0.63 0.64 17 0.13 6.7 23 1E4 6.6 54 1E4 Gurobi-BIGMIX 992 3600 1.31 1.28 1.31 1.19 1.17 17 0.13 6.7 23 1E4 6.6 54 1E4 SCIP-BIGMIX 1153 3600 0.77 0.67 0.72 0.58...

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mentioning

confidence: 99%

Algorithm runtime prediction: Methods & evaluation

Hutter

Hoos

et al. 2014

Artificial Intelligence

358

328

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“…Langley (1983a), Epstein et al (2002), Nareyek (2001) learn weights for decision rules to guide the selector towards the best algorithms. Cook and Varnell (1997), Guerri and Milano (2004), Guo and Hsu (2004), Roberts and Howe (2006), Bhowmick, Eijkhout, Freund, Fuentes, and Keyes (2006), Gent et al (2010a) go one step further and learn decision trees. Guo and Hsu (2004) again note that the reason for choosing decision trees was not primarily the performance, but the understandability of the result.…”

Section: Per-portfolio Modelsmentioning

confidence: 99%

“…Cook and Varnell (1997), Guerri and Milano (2004), Guo and Hsu (2004), Roberts and Howe (2006), Bhowmick, Eijkhout, Freund, Fuentes, and Keyes (2006), Gent et al (2010a) go one step further and learn decision trees. Guo and Hsu (2004) again note that the reason for choosing decision trees was not primarily the performance, but the understandability of the result. Pfahringer, Bensusan, and Giraud-Carrier (2000) show the set of learned rules in the paper to illustrate its compactness.…”

Section: Per-portfolio Modelsmentioning

confidence: 99%

“…They also report experimental results with sequential Bayesian linear regression and Gaussian Process regression. Guo (2003), Guo and Hsu (2004) explore using decision trees, naïve Bayes rules, Bayesian networks and meta-learning techniques. They also chose the C4.5 decision tree inducer because it is one of the top performers and creates models that are easy to understand and quick to execute.…”

Section: Selection Of Model Learnermentioning

confidence: 99%

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Algorithm Selection for Combinatorial Search Problems: A Survey

2014

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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.

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“…A Bayesian approach is proposed in (Guo, 2004) to construct an algorithm selection system which is applied to the Sorting and Most Probable Explanation (MPE) problems. From a set of training instances, their features and the run time of the best algorithm that solves each instance are utilized to build the Bayesian network.…”

Section: Machine Learningmentioning

confidence: 99%