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The Configurable SAT Solver Challenge (CSSC)

ARTIFICIAL INTELLIGENCE 2017年 243卷 1-25页

作者： Hutter, Frank Lindauer, Marius Balint, Adrian Bayless, Sam Hoos, Holger Leyton-Brown, Kevin Univ Freiburg Freiburg Germany Univ Ulm D-89069 Ulm Germany Univ British Columbia Vancouver BC Canada

It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Propositional satisfiability algorithm configuration Empirical evaluation Competition

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Automatic parameter tuning for functional regionalization methods

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PAPERS IN REGIONAL SCIENCE 2017年第4期96卷 859-+页

作者： Manuel Casado-Diaz, Jose Martinez-Bernabeu, Lucas Florez-Revuelta, Francisco Univ Alicante Inst Int Econ POB 99 E-03080 Alicante Spain Univ Alicante Dept Appl Econ Anal POB 99 E-03080 Alicante Spain Kingston Univ Fac Sci Engn & Comp Penrhyn Rd Kingston Upon Thames KT1 2EE Surrey England

The methods used to define functional regions for public statistics and policy purposes need to establish several parameter values. This is typically achieved using expert knowledge based on qualitative judgements and lengthy consultations with local stakeholders. We propose to support this process by using an optimization algorithm to calibrate any regionalization method by identifying the parameter values that produce the best regionalization for a given quantitative indicator. The approach is exemplified by using a grid search and a genetic algorithm to configure the official methods employed in the UK and Sweden for the definition of their respective official concepts of local labour markets.

关键词： Labour market areas functional regions parameter tuning regionalization travel-to-work areas algorithm configuration

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A Case Study on Automatic Parameter Optimization of a Mobile Robot Localization algorithm

A Case Study on Automatic Parameter Optimization of a Mobile...

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17th IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Lima, Oscar Ventura, Rodrigo Inst Super Tecn Inst Syst & Robot Ave Rovisco Pais 1 P-1049001 Lisbon Portugal

ISBN: (纸本)9781509062348

algorithms in robotics typically tend to expose several parameters for the user to configure. This allows both reusability and fine tuning of the algorithm to a particular setup, but at the expense of significant effort in tuning, since this task is typically done manually. However, recent results in parameter optimization have shown to be quite successful, namely in automatic configuration of boolean satisfiability problem solvers, contributing to a significant increase in scalability. In this paper we address the applicability of these methods to the area of mobile robot localization. In particular, we applied a sequential model-based optimization method to the automatic parameter tuning of the well-known Adaptive Monte Carlo Localization algorithm. Our results show a statistically significant improvement over the default algorithm values. We also contribute with an open source experimental setup, based on the popular Robot Operating System ROS, which can be easily adapted to other algorithms.

关键词： Model based optimization robotics black box optimization algorithm configuration automatic algorithm optimization multi objective parameter optimization automatic parameter tuning

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Feature Filtering for Instance-Specific algorithm configuration

Feature Filtering for Instance-Specific Algorithm Configurat...

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23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Kroer, Christian Malitsky, Yuri IT Univ Copenhagen Copenhagen Denmark Brown Univ Dept Comp Sci Providence RI USA

ISBN: (纸本)9780769545967

Instance-Specific algorithm configuration (ISAC) is a novel general technique for automatically generating and tuning algorithm portfolios. The approach has been very successful in practice, but up to now it has been committed to using all the features it was provided. However, traditional feature filtering techniques are not applicable, requiring multiple computationally expensive tuning steps during the evaluation stage. To this end, we show three new evaluation functions that use precomputed runtimes of a collection of untuned solvers to quickly evaluate subsets of features. One of our proposed functions even shows how to generate such an effective collection of solvers when only one highly parameterized solver is available. Using these new functions, we show that the number of features used by ISAC can be reduced to less than a quarter of the original number while often providing significant performance gains. We present numerical results on both SAT and CP domains.

关键词： feature selection algorithm configuration SAT CP

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On the Effectiveness of Automated configuration in Abstract Argumentation Reasoning

On the Effectiveness of Automated Configuration in Abstract ...

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6th International Conference on Computational Models of Argument (COMMA)

作者： Cerutti, Federico Vallati, Mauro Giacomin, Massimiliano Cardiff Univ Cardiff CF10 3AX S Glam Wales Univ Huddersfield Huddersfield HD1 3DH W Yorkshire England Univ Brescia I-25121 Brescia Italy

ISBN: (纸本)9781614996866

In this paper we investigate the impact of automated configuration techniques on the ArgSemSAT solver-runner-up of the ICCMA 2015-for solving the enumeration of preferred extensions. Moreover, we introduce a fully automated method for varying how argumentation frameworks are represented in the input file, and evaluate how the joint configuration of frameworks and ArgSemSAT parameters can have a remarkable impact on performance. Our findings suggest that automated configuration techniques lead to improved performances in argumentation solvers, an important message for participants to the forthcoming competition.

关键词： algorithm configuration Argumentation Framework configuration Abstract Argumentation

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Characterization of Neighborhood Behaviours in a Multi-neighborhood Local Search algorithm 10th

Characterization of Neighborhood Behaviours in a Multi-neigh...

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10th International Conference on Learning and Intelligent Optimization (LION)

作者： Nguyen Thi Thanh Dang De Causmaecker, Patrick KU Leuven KULAK CODeS iMinds ITEC Kortrijk Belgium

ISBN: (纸本)9783319503493;9783319503486

We consider a multi-neighborhood local search framework with a large number of possible neighborhoods. Each neighborhood is accompanied by a weight value which represents the probability of being chosen at each iteration. These weights are fixed before the algorithm runs, and can be tuned by off-the-shelf off-line automated algorithm configuration tools (e.g., SMAC). However, the large number of parameters might deteriorate the tuning tool's efficiency, especially in our case where each run of the algorithm is not computationally cheap, even when the number of parameters has been reduced by some intuition. In this work, we propose a systematic method to characterize each neighborhood's behaviours, representing them as a feature vector, and using cluster analysis to form similar groups of neighborhoods. The novelty of our characterization method is the ability of reflecting changes of behaviours according to hardness of different solution quality regions based on simple statistics collected during any algorithm runs. We show that using neighborhood clusters instead of individual neighborhoods helps to reduce the parameter configuration space without misleading the search of the tuning procedure. Moreover, this method is problem-independent and potentially can be applied in similar contexts.

关键词： algorithm configuration Clustering Multi-neighborhood local search

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RECIFE-MILP: An Effective MILP-Based Heuristic for the Real-Time Railway Traffic Management Problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2015年第5期16卷 2609-2619页

作者： Pellegrini, Paola Marliere, Gregory Pesenti, Raffaele Rodriguez, Joaquin Univ Lille Nord France F-59000 Lille France LEOST COSYS IFSTTAR F-59650 Villeneuve Dascq France ESTAS COSYS IFSTTAR F-59650 Villeneuve Dascq France Ca Foscari Univ I-30121 Venice Italy

The real-time railway traffic management problem consists of selecting appropriate train routes and schedules for minimizing the propagation of delay in case of traffic perturbation. In this paper, we tackle this problem by introducing RECIFE-MILP, a heuristic algorithm based on a mixed-integer linear programming model. RECIFE-MILP uses a model that extends one we previously proposed by including additional elements characterizing railway reality. In addition, it implements performance boosting methods selected among several ones through an algorithm configuration tool. We present a thorough experimental analysis that shows that the performances of RECIFE-MILP are better than the ones of the currently implemented traffic management strategy. RECIFE-MILP often finds the optimal solution to instances within the short computation time available in real-time applications. Moreover, RECIFE-MILP is robust to its configuration if an appropriate selection of the combination of boosting methods is performed.

关键词： Real-time railway traffic management problem mixed-integer linear programming algorithm configuration performance boosting

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Automatic model selection for high-dimensional survival analysis

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2015年第1期85卷 62-76页

作者： Lang, M. Kotthaus, H. Marwedel, P. Weihs, C. Rahnenfuehrer, J. Bischl, B. TU Dortmund Univ Dept Stat D-44227 Dortmund Germany TU Dortmund Univ Dept Comp Sci 12 D-44227 Dortmund Germany

Many different models for the analysis of high-dimensional survival data have been developed over the past years. While some of the models and implementations come with an internal parameter tuning automatism, others require the user to accurately adjust defaults, which often feels like a guessing game. Exhaustively trying out all model and parameter combinations will quickly become tedious or infeasible in computationally intensive settings, even if parallelization is employed. Therefore, we propose to use modern algorithm configuration techniques, e.g. iterated F-racing, to efficiently move through the model hypothesis space and to simultaneously configure algorithm classes and their respective hyperparameters. In our application we study four lung cancer microarray data sets. For these we configure a predictor based on five survival analysis algorithms in combination with eight feature selection filters. We parallelize the optimization and all comparison experiments with the BatchJobs and BatchExperiments R packages.

关键词： model selection feature selection algorithm configuration survival analysis machine learning high-dimensional data racing parameter tuning

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Scalable Nearest Neighbor algorithms for High Dimensional Data

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2014年第11期36卷 2227-2240页

作者： Muja, Marius Lowe, David G. BitLit Media Inc Vancouver BC Canada Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

关键词： Nearest neighbor search big data approximate search algorithm configuration

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Automatic (offline) configuration of algorithms

Automatic (offline) configuration of algorithms

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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Sẗutzle, Thomas López-Ibáñez, Manuel Brussels Belgium

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