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MaxSAT by improved instance-specific algorithm configuration

ARTIFICIAL INTELLIGENCE 2016年 235卷 26-39页

作者： Ansotegui, Carlos Gabas, Joel Malitsky, Yuri Sellmann, Meinolf Univ Lleida Dept Comp Sci Lleida Spain IBM Watson Res Ctr Yorktown Hts NY 10598 USA

Our objective is to boost the state-of-the-art performance in MaxSAT solving. To this end, we employ the instance-specific algorithm configurator ISAC, and improve it with the latest in portfolio technology. Experimental results on SAT show that this combination marks a significant step forward in our ability to tune algorithms instance-specifically. We then apply the new methodology to a number of MaxSAT problem domains and show that the resulting solvers consistently outperform the best existing solvers on the respective problem families. In fact, the solvers presented here were independently evaluated at the 2013 and 2014 MaxSAT Evaluations where they won several categories. (C) 2016 Elsevier B.V. All rights reserved.

关键词： algorithm configuration algorithm selection MaxSAT

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algorithm portfolios for noisy optimization

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2016年第1-2期76卷 143-172页

作者： Cauwet, Marie-Liesse Liu, Jialin Roziere, Baptiste Teytaud, Olivier Univ Paris 11 INRIA CNRS LRI TAO F-91190 Gif Sur Yvette France

Noisy optimization is the optimization of objective functions corrupted by noise. A portfolio of solvers is a set of solvers equipped with an algorithm selection tool for distributing the computational power among them. Portfolios are widely and successfully used in combinatorial optimization. In this work, we study portfolios of noisy optimization solvers. We obtain mathematically proved performance (in the sense that the portfolio performs nearly as well as the best of its solvers) by an ad hoc portfolio algorithm dedicated to noisy optimization. A somehow surprising result is that it is better to compare solvers with some lag, i.e., propose the current recommendation of best solver based on their performance earlier in the run. An additional finding is a principled method for distributing the computational power among solvers in the portfolio.

关键词： Black-box noisy optimization algorithm selection Simple regret

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Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers 10th

Evolving Instances for Maximizing Performance Differences of...

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

作者： Bossek, Jakob Trautmann, Heike Univ Munster Dept Informat Syst Munster Germany

ISBN: (纸本)9783319503493;9783319503486

Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.

关键词： TSP Instance hardness algorithm selection Feature selection

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A Meta-Learning Method to Select Under-Sampling algorithms for Imbalanced Data Sets 5

A Meta-Learning Method to Select Under-Sampling Algorithms f...

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5th Brazilian Conference on Intelligent Systems (BRACIS)

作者： de Morais, Romero F. A. B. Miranda, Pericles B. C. Silva, Ricardo M. A. Univ Fed Pernambuco Recife PE Brazil

ISBN: (纸本)9781509035663

Imbalanced data sets originating from real world problems, such as medical diagnosis, can be found pervasive. Learning from imbalanced data sets poses its own challenges, as common classifiers assume a balanced distribution of examples' classes in the data. Sampling techniques overcome the imbalance in the data by modifying the examples' classes distribution. Unfortunately, selecting a sampling technique together with its parameters is still an open problem. Current solutions include the brute-force approach (try as many techniques as possible), and the random search approach (choose the most appropriate from a random subset of techniques). In this work, we propose a new method to select sampling techniques for imbalanced data sets. It uses Meta-Learning and works by recommending a technique for an imbalanced data set based on solutions to previous problems. Our experimentation compared the proposed method against the brute-force approach, all techniques with their default parameters, and the random search approach. The results of our experimentation show that the proposed method is comparable to the brute-force approach, outperforms the techniques with their default parameters most of the time, and always surpasses the random search approach.

关键词： Meta-learning algorithm selection Sampling algorithms

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Towards Automatic Generation of Metafeatures 20th

Towards Automatic Generation of Metafeatures

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20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Pinto, Fabio Soares, Carlos Mendes-Moreira, Joao Univ Porto Fac Engn INESC TEC Rua Dr Roberto Frias S-N P-4200465 Oporto Portugal

ISBN: (纸本)9783319317533;9783319317526

The selection of metafeatures for metalearning (MtL) is often an ad hoc process. The lack of a proper motivation for the choice of a metafeature rather than others is questionable and may originate a loss of valuable information for a given problem (e.g., use of class entropy and not attribute entropy). We present a framework to systematically generate metafeatures in the context of MtL. This framework decomposes a metafeature into three components: meta-function, object and post-processing. The automatic generation of metafeatures is triggered by the selection of a meta-function used to systematically generate metafeatures from all possible combinations of object and post-processing alternatives. We executed experiments by addressing the problem of algorithm selection in classification datasets. Results show that the sets of systematic metafeatures generated from our framework are more informative than the non-systematic ones and the set regarded as state-of-the-art.

关键词： Metalearning Systematic metafeatures algorithm selection Classification

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An Empirical Study of Per-instance algorithm Scheduling 10th

An Empirical Study of Per-instance Algorithm Scheduling

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

作者： Lindauer, Marius Bergdoll, Rolf-David Hutter, Frank Univ Freiburg Freiburg Germany

ISBN: (纸本)9783319503493;9783319503486

algorithm selection is a prominent approach to improve a system's performance by selecting a well-performing algorithm from a portfolio for an instance at hand. One extension of the traditional algorithm selection problem is to not only select one single algorithm but a schedule of algorithms to increase robustness. Some approaches exist for solving this problem of selecting schedules on a per-instance basis (e. g., the Sunny and 3S systems), but to date, a fair and thorough comparison of these is missing. In this work, we implement Sunny's approach and dynamic schedules inspired by 3S in the flexible algorithm selection framework flexfolio to use the same code base for a fair comparison. Based on the algorithm selection library (ASlib), we perform the first thorough empirical study on the strengths and weaknesses of per-instance algorithm schedules. We observe that on some domains it is crucial to use a training phase to limit the maximal size of schedules and to select the optimal neighborhood size of k-nearest-neighbor. By modifying our implemented variants of the Sunny and 3S approaches in this way, we achieve strong performance on many ASlib benchmarks and establish new state-of-the-art performance on 3 scenarios.

关键词： algorithm selection algorithm schedules Constraint solving

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A tensor-based selection hyper-heuristic for cross-domain heuristic search

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INFORMATION SCIENCES 2015年 299卷 412-432页

作者： Asta, Shahriar Oezcan, Ender Univ Nottingham Sch Comp Sci ASAP Res Grp Nottingham NG8 1BB England

Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Hyper-heuristic Data science Machine learning Move acceptance Tensor analysis algorithm selection

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algorithm portfolio selection as a bandit problem with unbounded losses

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2011年第2期61卷 49-86页

作者： Gagliolo, Matteo Schmidhuber, Jurgen Vrije Univ Brussel CoMo B-1050 Brussels Belgium IDSIA CH-6928 Lugano Switzerland Univ Lugano Fac Informat CH-6904 Lugano Switzerland

We propose a method that learns to allocate computation time to a given set of algorithms, of unknown performance, with the aim of solving a given sequence of problem instances in a minimum time. Analogous meta-learning techniques are typically based on models of algorithm performance, learned during a separate offline training sequence, which can be prohibitively expensive. We adopt instead an online approach, named GAMBLETA, in which algorithm performance models are iteratively updated, and used to guide allocation on a sequence of problem instances. GAMBLETA is a general method for selecting among two or more alternative algorithm portfolios. Each portfolio has its own way of allocating computation time to the available algorithms, possibly based on performance models, in which case its performance is expected to improve over time, as more runtime data becomes available. The resulting exploration-exploitation trade-off is represented as a bandit problem. In our previous work, the algorithms corresponded to the arms of the bandit, and allocations evaluated by the different portfolios were mixed, using a solver for the bandit problem with expert advice, but this required the setting of an arbitrary bound on algorithm runtimes, invalidating the optimal regret of the solver. In this paper, we propose a simpler version of GAMBLETA, in which the allocators correspond to the arms, such that a single portfolio is selected for each instance. The selection is represented as a bandit problem with partial information, and an unknown bound on losses. We devise a solver for this game, proving a bound on its expected regret. We present experiments based on results from several solver competitions, in various domains, comparing GAMBLETA with another online method.

关键词： algorithm selection algorithm portfolios Meta learning Online learning Multi-armed bandit problem Survival analysis Las Vegas algorithms Computational complexity Combinatorial optimization Constraint programming Satisfiability

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Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources

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JOURNAL OF HAZARDOUS MATERIALS 2015年 299卷 260-279页

作者： Chen, Baiyang Zhang, Tian Bond, Tom Gan, Yiqun Harbin Inst Technol Shenzhen Grad Sch Shenzhen Key Lab Water Resource Utilizat & Enviro Shenzhen 518055 Peoples R China Univ London Imperial Coll Sci Technol & Med Dept Civil & Environm Engn London SW7 2AZ England

Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation;and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Disinfection byproduct QSAR/QSPR/QSTR/LFER Descriptor filtration algorithm selection Model validation

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The glaciers climate change initiative: Methods for creating glacier area, elevation change and velocity products

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REMOTE SENSING OF ENVIRONMENT 2015年 162卷 408-426页

作者： Paul, Frank Bolch, Tobias Kaab, Andreas Nagler, Thomas Nuth, Christopher Scharrer, Killian Shepherd, Andrew Strozzi, Tazio Ticconi, Francesca Bhambri, Rakesh Berthier, Etienne Bevan, Suzanne Gourmelen, Noel Heid, Torborg Jeong, Seongsu Kunz, Matthias Lauknes, Tom Rune Luckman, Adrian Boncori, John Peter Merryman Moholdt, Geir Muir, Alan Neelmeijer, Julia Rankl, Melanie VanLooy, Jeffrey Van Niel, Thomas Univ Zurich Dept Geog CH-8006 Zurich Switzerland Univ Oslo Dept Geosci N-0316 Oslo Norway Environm Earth Observat IT GmbH ENVEO Innsbruck Austria Univ Leeds Sch Earth & Environm Leeds LS2 9JT W Yorkshire England Gamma Remote Sensing AG Gumlingen Switzerland Wadia Inst Himalayan Geol Ctr Glaciol Dehra Dun 248001 Uttar Pradesh India CNRS OMP LEGOS Toulouse France Swansea Univ Swansea W Glam Wales Univ Strasbourg Inst Phys Globe Strasbourg Strasbourg France Ohio State Univ Columbus OH 43210 USA Newcastle Univ Sch Civil Engn & Geosci Newcastle Upon Tyne Tyne & Wear England Norut Tromso Norway Swansea Univ Swansea W Glam Wales Ist Nazl Geofis & Vulcanol Rome Italy Scripps Inst Oceanog La Jolla GA USA Ctr Polar Observat & Modelling London England GFZ German Res Ctr Geosci Helmholtz Ctr Potsdam Potsdam Germany Univ Erlangen Nurnberg Erlangen Germany Univ N Dakota Dept Earth Syst Sci & Policy Grand Forks ND 58201 USA CSIRO Land & Water Wembley WA Australia

Glaciers and their changes through time are increasingly obtained from a wide range of satellite sensors. Due to the often remote location of glaciers in inaccessible and high-mountain terrain, satellite observations frequently provide the only available measurements. Furthermore, satellite data provide observations of glacier characteristics that are difficult to monitor using ground-based measurements, thus complementing the latter. In the Glaciers_cci project of the European Space Agency (ESA), three of these characteristics are investigated in detail: glacier area, elevation change and surface velocity. We use (a) data from optical sensors to derive glacier outlines, (b) digital elevation models from at least two points in time, (c) repeat altimetry for determining elevation changes, and (d) data from repeat optical and microwave sensors for calculating surface velocity. For the latter, the two sensor types provide complementary information in terms of spatio-temporal coverage. While (c) and (d) can be generated mostly automatically, (a) and (b) require the intervention of an analyst. Largely based on the results of various round robin experiments (multi-analyst benchmark studies) for each of the products, we suggest and describe the most suitable algorithms for product creation and provide recommendations concerning their practical implementation and the required post-processing. For some of the products (area, velocity) post-processing can influence product quality more than the main-processing algorithm. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Glacier area Glacier elevation change Glacier velocity Optical and microwave remote sensing Round robin experiment Accuracy determination algorithm selection

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