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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.
Continuous Search in Constraint Programming
"... Abstract—This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user’s problem instances using the cu ..."
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Cited by 2 (0 self)
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Abstract—This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user’s problem instances using the current heuristics model; the exploration mode reuses these instances to train and improve the heuristics model through Machine Learning during the computer idle time. Contrasting with previous approaches, Continuous Search thus does not require that the representative instances needed to train a good heuristics model be available beforehand. It achieves lifelong learning, gradually becoming an expert on the user’s problem instance distribution. Experimental validation suggests that Continuous Search can design efficient mixed strategies after considering a moderate number of problem instances. I.
An Enhanced Features Extractor for a Portfolio of Constraint Solvers. http://www.cs.unibo.it/ ~amadini/sac_2014.pdf
 In SAC
, 2014
"... Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower onaverage solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specifica ..."
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Cited by 2 (2 self)
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Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower onaverage solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results showing that the performances that can be obtained using these features are effective and competitive with state of the art CSP portfolio techniques. 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 ..."
Abstract
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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.
Continuous Search in Constraint Programming: An Initial Investigation
"... Abstract. In this work, we present the concept of Continuous Search, the objective of which is to allow any user to eventually get top performance from their constraint solver. Unlike previous approaches (see [9] for a recent survey), Continuous Search does not require the disposal of a large set of ..."
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Abstract. In this work, we present the concept of Continuous Search, the objective of which is to allow any user to eventually get top performance from their constraint solver. Unlike previous approaches (see [9] for a recent survey), Continuous Search does not require the disposal of a large set of representative instances to properly train and learn parameters. It only assumes that once the solver runs in a real situation (often called production mode), instances will come over time, and allow for proper offline continuous training. The objective is therefore not to instantly provide good parameters for top performance, but to take advantage of the real situation to train in the background and improve the performances of the system in an incremental manner. 1
Author manuscript, published in "22th International Conference on Tools with Artificial Intelligence (2010)" Continuous Search in Constraint Programming
, 2010
"... Abstract—This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user’s problem instances using the cu ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract—This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user’s problem instances using the current heuristics model; the exploration mode reuses these instances to train and improve the heuristics model through Machine Learning during the computer idle time. Contrasting with previous approaches, Continuous Search thus does not require that the representative instances needed to train a good heuristics model be available beforehand. It achieves lifelong learning, gradually becoming an expert on the user’s problem instance distribution. Experimental validation suggests that Continuous Search can design efficient mixed strategies after considering a moderate number of problem instances. I.