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检索条件"主题词=algorithm selection"

共 313 条记录，以下是271-280 订阅

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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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Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES Variants

Integrated vs. Sequential Approaches for Selecting and Tunin...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick Wang, Hao Doerr, Carola Back, Thomas Leiden Inst Adv Comp Sci Leiden Netherlands Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781450371285

When faced with a specific optimization problem, deciding which algorithm to apply is always a difficult task. Not only is there a vast variety of algorithms to select from, but these algorithms are often controlled by many hyperparameters, which need to be suitably tuned in order to achieve peak performance. Usually, the problem of selecting and configuring the optimization algorithm is addressed sequentially, by first selecting a suitable algorithm and then tuning it for the application at hand. Integrated approaches, commonly known as Combined algorithm selection and Hyperparameter (CASH) solvers, have shown promise in several applications. In this work we compare sequential and integrated approaches for selecting and tuning the best out of the 4,608 variants of the modular Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We show that the ranking of these variants depends to a large extent on the quality of the hyperparameters. Sequential approaches are therefore likely to recommend sub-optimal choices. Integrated approaches, in contrast, manage to provide competitive results at much smaller computational cost. We also highlight important differences in the search behavior of two CASH approaches, which build on racing (irace) and on model-based optimization (MIP-EGO), respectively.

关键词： algorithm selection algorithm Control Iterative Optimization Heuristics Black-Box Optimization

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Self-Organising algorithms for Residential Demand Response 2

Self-Organising Algorithms for Residential Demand Response

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EEE Conference on Technologies for Sustainability (SusTech)

作者： Taylor, Adam Dusparic, Ivana Harris, Colin Marinescu, Andrei Galvan-Lopez, Edgar Golpayegani, Fatemeh Clarke, Siobhan Cahill, Vinny Trinity Coll Dublin Sch Comp Sci & Stat Distributed Syst Grp Dublin Ireland

ISBN: (纸本)9781479952380

Residential Demand Response has shown promising results in smart grid applications. It can be achieved manually or autonomously. The variety of algorithms applied to achieve autonomous Demand Response have lacked a common baseline. The selection of testing environment is typically skewed by the desire to find one in which a particular algorithm performs well. This work evaluates several algorithms in a common evaluation environment. Which has been designed to encompass the range of conditions in a typical Residential Demand Response application. The environments described exhibit the following characteristics: non-stationary, dynamic, multi-actor, multi-objective. These characteristics will then be used to provide heuristics for algorithm selection. The algorithms used were selected to cover the spectrum of possible approaches to Demand Response. Some are centralised, others distributed. There are collaborative approaches and non-collaborative ones. Some are learning based, others require no training. This work provides criteria for which particular algorithms should be applied to a given application.

关键词： demand side management power engineering computing self-organising feature maps algorithm selection collaborative approaches common evaluation environment residential demand response self-organising algorithms smart grid algorithm design and analysis Collaboration Heuristic algorithms Load management Prediction algorithms Schedules Water heating

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Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization 20

Auto-Surprise: An Automated Recommender-System (AutoRecSys) ...

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14th ACM Conference on Recommender Systems (RECSYS)

作者： Anand, Rohan Beel, Joeran Trinity Coll Dublin Dublin Ireland

ISBN: (纸本)9781450375832

We introduce Auto-Surprise(1), an automated recommender system library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to an out-of-the-box Surprise library, without hyper parameter optimization, AutoSurprise performs better, when evaluated with MovieLens, Book Crossing and Jester datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.

关键词： AutoRecSys AutoML algorithm selection hyperparameter optimization

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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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Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit 23

Introducing LensKit-Auto, an Experimental Automated Recommen...

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17th ACM Conference on Recommender Systems (RecSys)

作者： Vente, Tobias Ekstrand, Michael D. Beel, Joeran Univ Siegen Intelligent Syst Grp Siegen Germany Boise State Univ Boise ID USA

ISBN: (纸本)9798400702419

LensKit is one of the first and most popular Recommender System libraries. While LensKit offers a wide variety of features, it does not include any optimization strategies or guidelines on how to select and tune LensKit algorithms. LensKit developers have to manually include third-party libraries into their experimental setup or implement optimization strategies by hand to optimize hyperparameters. We found that 63.6% (21 out of 33) of papers using LensKit algorithms for their experiments did not select algorithms or tune hyperparameters. Non-optimized models represent poor baselines and produce less meaningful research results. This demo introduces LensKit-Auto. LensKit-Auto automates the entire Recommender System pipeline and enables LensKit developers to automatically select, optimize, and ensemble LensKit algorithms.

关键词： Recommender Systems Automated Recommender Systems AutoRecSys algorithm selection Hyperparameter Optimization CASH

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How Far Out of Distribution Can We Go With ELA Features and Still Be Able to Rank algorithms?

How Far Out of Distribution Can We Go With ELA Features and ...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Petelin, Gasper Cenikj, Gjorgjina Jožef Stefan Institute Jožef Stefan International Postgraduate School Computer Systems Department Ljubljana Slovenia

ISBN: (纸本)9781665430654

algorithm selection is a critical aspect of continuous black-box optimization, and various methods have been proposed to choose the most appropriate algorithm for a given problem. One commonly used approach involves employing Exploratory Landscape Analysis (ELA) features to represent optimization functions and training a machine-learning meta-model to perform algorithm selections based on these features. However, many meta-models trained on existing benchmarks suffer from limited generalizability. When faced with a new optimization function, these meta-models often struggle to select the most suitable algorithm, restricting their practical application. In this study, we investigate the generalizability of meta-models when tested on previously unseen functions that were not observed during training. Specifically, we train a meta-model on base COmparing Continuous Optimizers (COCO) functions and evaluate its performance on new functions derived as affine combinations between pairs of the base functions. Our findings demonstrate that the task of ranking algorithms becomes substantially more challenging when the functions differ from those encountered during meta-learning training. This indicates that the effectiveness of algorithm selection diminishes when confronted with problem instances that substantially deviate from the training distribution. In such scenarios, meta-models that use ELA features to predict algorithm ranks do not outperform mere predictions of the average algorithm ranks. © 2023 IEEE.

关键词： algorithm selection black-box optimization evolutionary computation exploratory landscape analysis

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Characterization of CEC Single-Objective Optimization Competition Benchmarks and algorithms

Characterization of CEC Single-Objective Optimization Compet...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Misir, Mustafa Duke Kunshan University Division of Natural and Applied Sciences Kunshan China

ISBN: (纸本)9781665430654

The present study provides an analysis on the characteristics of single-objective optimization benchmark problems as well as the algorithms used to solve them. The target optimization domain involves the CEC competitions, each consisting a set of mathematical functions. Concerning the optimization tasks, the idea is to investigate the dis/-similarities between different competition scenarios and individual benchmarks. For the solvers, the goal is to detect the dis/-similarities between the algorithms applied to the CEC benchmarks. Those analysis missions are carried out by using the features directly and automatically extracted from the performance data, the quality of the solutions achieved by each algorithm on the benchmarks. The feature extraction process is realized through Singular Value Decomposition. Following the analysis on the algorithms, the potential of algorithm selection has been evaluated to see the performance improvement without actually developing a new algorithm, against those 20 algorithms. © 2023 IEEE.

关键词： algorithm selection Single-objective Optimization Singular Value Decomposition

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Automatic object detection in point clouds based on knowledge guided algorithms

Automatic object detection in point clouds based on knowledg...

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Conference on Videometrics, Range Imaging, and Applications XII;and Automated Visual Inspection

作者： Hung Truong Karmacharya, Ashish Mordwinzew, Waldemar Boochs, Frank Chudyk, Celeste Habed, Adlane Voisin, Yvon Univ Appl Sci Mainz I3mainz Lucy Hillebrand Str 2 D-55128 Mainz Germany Univ Burgundy Le2i F-89010 Auxerre France

ISBN: (纸本)9780819496072

The modeling of real-world scenarios through capturing 3D digital data has been proven applicable in a variety of industrial applications, ranging from security, to robotics and to fields in the medical sciences. These different scenarios, along with variable conditions, present a challenge in discovering flexible appropriate solutions. In this paper, we present a novel approach based on a human cognition model to guide processing. Our method turns traditional data-driven processing into a new strategy based on a semantic knowledge system. Robust and adaptive methods for object extraction and identification are modeled in a knowledge domain, which has been created by purely numerical strategies. The goal of the present work is to select and guide algorithms following adaptive and intelligent manners for detecting objects in point clouds. Results show that our approach succeeded in identifying the objects of interest while using various data types.

关键词： Object detection segmentation algorithm selection knowledge-based system labeling

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Graves-CPA: A Graph-Attention Verifier Selector (Competition Contribution) 28th

Graves-CPA: A Graph-Attention Verifier Selector (Competition...

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28th International Conference on Tools and algorithms for the Construction and Analysis of Systems (TACAS) Held as Part of the 25th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Leeson, Will Dwyer, Matthew B. Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9783030995270;9783030995263

GRAVES-CPA is a verification tool which uses algorithm selection to decide an ordering of underlying verifiers to most effectively verify a given program. GRAVES-CPA represents programs using an amalgam of traditional program graph representations and uses stateof-the-art graph neural network techniques to dynamically decide how to run a set of verification techniques. The GRAVES technique is implementation agnostic, but it's competition submission, GRAVES-CPA, is built using several CPAchecker configurations as its underlying verifiers.

关键词： Software Verification Graph Attention Networks Graph Neural Networks algorithm selection

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