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11
Algorithm Selection for Combinatorial Search Problems: A Survey
, 2012
"... The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase 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 prob ..."
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Cited by 20 (5 self)
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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase 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.
Evaluating Component Solver Contributions to PortfolioBased Algorithm Selectors
"... Abstract. Portfoliobased methods exploit the complementary strengths of a set of algorithms and—as evidenced in recent competitions—represent the state of the art for solving many NPhard problems, including SAT. In this work, we argue that a stateoftheart method for constructing portfoliobased ..."
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Abstract. Portfoliobased methods exploit the complementary strengths of a set of algorithms and—as evidenced in recent competitions—represent the state of the art for solving many NPhard problems, including SAT. In this work, we argue that a stateoftheart method for constructing portfoliobased algorithm selectors, SATzilla, also gives rise to an automated method for quantifying the importance of each of a set of available solvers. We entered a substantially improved version of SATzilla to the inaugural “analysis track ” of the 2011 SAT competition, and draw two main conclusions from the results that we obtained. First, automaticallyconstructed portfolios of sequential, nonportfolio competition entries perform substantially better than the winners of all three sequential categories. Second, and more importantly, a detailed analysis of these portfolios yields valuable insights into the nature of successful solver designs in the different categories. For example, we show that the solvers contributing most to SATzilla were often not the overall bestperforming solvers, but instead solvers that exploit novel solution strategies to solve instances that would remain unsolved without them. 1
HydraMIP: Automated Algorithm Configuration and Selection for Mixed Integer Programming
"... Abstract. Stateoftheart mixed integer programming (MIP) solvers are highly parameterized. For heterogeneous and a priori unknown instance distributions, no single parameter configuration generally achieves consistently strong performance, and hence it is useful to select from a portfolio of diffe ..."
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Cited by 12 (6 self)
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Abstract. Stateoftheart mixed integer programming (MIP) solvers are highly parameterized. For heterogeneous and a priori unknown instance distributions, no single parameter configuration generally achieves consistently strong performance, and hence it is useful to select from a portfolio of different configurations. HYDRA is a recent method for using automated algorithm configuration to derive multiple configurations of a single parameterized algorithm for use with portfoliobased selection. This paper shows that, leveraging two key innovations, HYDRA can achieve strong performance for MIP. First, we describe a new algorithm selection approach based on classification with a nonuniform loss function, which significantly improves the performance of algorithm selection for MIP (and SAT). Second, by modifying HYDRA’s method for selecting candidate configurations, we obtain better performance as a function of training time. 1
Algorithm portfolios based on costsensitive hierarchical clustering.
 Proc. of IJCAI’13.
, 2013
"... Abstract Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedule ..."
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Cited by 5 (0 self)
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Abstract Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedules solvers dependent on the given instance. We devise a new classifier that selects solvers based on a costsensitive hierarchical clustering model. Experimental results on SAT and MaxSAT show that the new method outperforms the most effective portfolio builders to date.
Learning Algorithm Portfolios for Parallel Execution
 IN PROC. OF LION
"... Portfoliobased solvers are both effective and robust, but their promise for parallel execution with constraint satisfaction solvers has received relatively little attention. This paper proposes an approach that constructs algorithm portfolios intended for parallel execution based on a combination ..."
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Cited by 4 (2 self)
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Portfoliobased solvers are both effective and robust, but their promise for parallel execution with constraint satisfaction solvers has received relatively little attention. This paper proposes an approach that constructs algorithm portfolios intended for parallel execution based on a combination of casebased reasoning, a greedy algorithm, and three heuristics. Empirical results show that this method is efficient, and can significantly improve performance with only a few additional processors. On problems from solver competitions, the resultant algorithm portfolios perform nearly as well as an oracle.
Snappy: A simple algorithm portfolio  (tool paper
 In International Conference on Theory and Applications of Satisfiability Testing (SAT’13), LNCS
"... Abstract. Algorithm portfolios try to combine the strength of individual algorithms to tackle a problem instance at hand with the most suitable technique. In the context of SAT the effectiveness of such approaches is often demonstrated at the SAT Competitions. In this paper we show that a competitiv ..."
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Abstract. Algorithm portfolios try to combine the strength of individual algorithms to tackle a problem instance at hand with the most suitable technique. In the context of SAT the effectiveness of such approaches is often demonstrated at the SAT Competitions. In this paper we show that a competitive algorithm portfolio can be designed in an extremely simple fashion. In fact, the algorithm portfolio we present does not require any offline learning nor knowledge of any complex Machine Learning tools. We hope that the utter simplicity of our approach combined with its effectiveness will make algorithm portfolios accessible by a broader range of researchers including SAT and CSP solver developers. 1
Algorithm Selection for Search: A survey Algorithm Selection for Combinatorial Search Problems: A survey
"... Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solv ..."
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Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase 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.
Automated Design of Search with Composability
"... Automated algorithm configuration aims at automatically parameterizing an algorithm or metaalgorithm when given a problem instance (or class) so that it performs most effectively on that instance. The need for algorithm configuration ..."
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Automated algorithm configuration aims at automatically parameterizing an algorithm or metaalgorithm when given a problem instance (or class) so that it performs most effectively on that instance. The need for algorithm configuration
Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence Algorithm Portfolios Based on CostSensitive Hierarchical Clustering
"... Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedules solvers ..."
Abstract
 Add to MetaCart
Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedules solvers dependent on the given instance. We devise a new classifier that selects solvers based on a costsensitive hierarchical clustering model. Experimental results on SAT and MaxSAT show that the new method outperforms the most effective portfolio builders to date. 1