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Kotthoff L.,Cork Constraint Computation Center
AI Magazine | Year: 2014

The algorithm selection problem is concerned with selecting the best algorithm to solve a given problem instance on a case-bycase basis. It has become especially relevant in the last decade, with researchers increasingly investigating how to identify the most suitable existing algorithm for solving a problem instance 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 analyzes the different directions from which algorithm selection has been approached. This article contrasts and compares different methods for solving the problem as well as ways of using these solutions. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved. Source

Bistarelli S.,University of Perugia | Bistarelli S.,CNR Institute for Informatics and Telematics | Bistarelli S.,University of Chieti Pescara | Foley S.N.,University College Cork | And 4 more authors.
Security and Communication Networks | Year: 2010

Multitrust provides a flexible approach to encoding trust metrics whereby definitions for trust propagation and aggregation are specified in terms of a semiring. Determining the degree of trust between principals across a trust network (TN) is, in turn, programmed as a (semiring-based) soft-constraint satisfaction problem. In this paper, we consider the use of semiring-based metrics in reasoning about trust between coalition-forming principals. The configurable nature of multitrust makes it well-suited to modeling trust within coalitions: whether adding more principals to a coalition increases trust or decreases trust is captured by the definition of trust aggregation within the semiring. Copyright © 2010 John Wiley & Sons, Ltd. Multitrust provides a flexible approach to encoding trust metrics whereby definitions for trust propagation and aggregation are specified in terms of a semiring. © 2010 John Wiley & Sons, Ltd. Source

Abell T.,IT University of Copenhagen | Malitsky Y.,Cork Constraint Computation Center | Tierney K.,IT University of Copenhagen
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Black-box optimization (BBO) problems arise in numerous scientific and engineering applications and are characterized by computationally intensive objective functions, which severely limit the number of evaluations that can be performed. We present a robust set of features that analyze the fitness landscape of BBO problems and show how an algorithm portfolio approach can exploit these general, problem independent, features and outperform the utilization of any single minimization search strategy. We test our methodology on data from the GECCO Workshop on BBO Benchmarking 2012, which contains 21 state-of-the-art solvers run on 24 well-established functions. © 2013 Springer-Verlag. Source

Akgun O.,University of St. Andrews | Frisch A.M.,University of York | Gent I.P.,University of St. Andrews | Hussain B.S.,University of St. Andrews | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Constraint modelling is widely recognised as a key bottleneck in applying constraint solving to a problem of interest. The Conjure automated constraint modelling system addresses this problem by automatically refining constraint models from problem specifications written in the Essence language. Essence provides familiar mathematical concepts like sets, functions and relations nested to any depth. To date, Conjure has been able to produce a set of alternative model kernels (i.e. without advanced features such as symmetry breaking or implied constraints) for a given specification. The first contribution of this paper is a method by which Conjure can break symmetry in a model as it is introduced by the modelling process. This works at the problem class level, rather than just individual instances, and does not require an expensive detection step after the model has been formulated. This allows Conjure to produce a higher quality set of models. A further limitation of Conjure has been the lack of a mechanism to select among the models it produces. The second contribution of this paper is to present two such mechanisms, allowing effective models to be chosen automatically. © 2013 Springer-Verlag. Source

Prestwich S.D.,Cork Constraint Computation Center | Tarim S.A.,Hacettepe University | Rossi R.,Decision and Information science Group | Hnich B.,Izmir University of Economics
International Journal of Production Research | Year: 2012

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form. © 2012 Copyright Taylor and Francis Group, LLC. Source

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