检索结果-内蒙古大学图书馆

automated algorithm selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning

引用

EVOLUTIONARY COMPUTATION 2019年第1期27卷 99-127页

作者： Kerschke, Pascal Trautmann, Heike Univ Munster Informat Syst & Stat D-48149 Munster Germany

In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.

关键词： automated algorithm selection black-box optimization exploratory landscape analysis machine learning single-objective continuous optimization

来源：评论

学校读者我要写书评

暂无评论

automated algorithm selection: Survey and Perspectives

引用

EVOLUTIONARY COMPUTATION 2019年第1期27卷 3-45页

作者： Kerschke, Pascal Hoos, Holger H. Neumann, Frank Trautmann, Heike Univ Munster Informat Syst & Stat D-48149 Munster Germany Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands Univ Adelaide Optimisat & Logist Adelaide SA 5005 Australia

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art;instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

关键词： automated algorithm selection automated algorithm configuration combinatorial optimisation continuous optimisation machine learning metalearning feature-based approaches exploratory landscape analysis data streams

来源：评论

学校读者我要写书评

暂无评论

automated algorithm selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods 17th

Automated Algorithm Selection in Single-Objective Continuous...

引用

17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Prager, Raphael Patrick Seiler, Moritz Vinzent Trautmann, Heike Kerschke, Pascal Univ Munster Data Sci Stat & Optimizat Munster Germany Univ Twente Data Management & Biometr Enschede Netherlands Tech Univ Dresden Big Data Analyt Transportat Dresden Germany

ISBN: (纸本)9783031147142;9783031147135

In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.

关键词： automated algorithm selection Exploratory landscape analysis Deep learning Continuous optimization

来源：评论

学校读者我要写书评

暂无评论

A DRL-Based automated algorithm selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

引用

IEEE ACCESS 2023年 11卷 13243-13256页

作者： Huang, Chih-Wei Althamary, Ibrahim Chou, Yen-Cheng Chen, Hong-Yunn Chou, Cheng-Fu Natl Cent Univ Dept Commun Engn Taoyuan Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei 10617 Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10617 Taiwan

Massive multiple-input-multiple-output (MIMO) systems support advanced applications with high data rates, low latency, and high reliability in next-generation mobile networks. However, using machine learning in massive MIMO resource allocation is challenging due to quality of service (QoS) and network complexity across layers. This work presents a novel framework for adapting the scheduling and antenna allocation in massive MIMO systems using deep reinforcement learning (DRL). Rather than directly assigning execution parameters, the proposed framework utilizes DRL to select combinations of algorithms based on the current traffic conditions. The DRL model is trained using a specialized Markov decision process (MDP) model with a componentized action structure and is evaluated in realistic traffic scenarios. The results show that the proposed framework increases satisfied users by 7.2% and 12.5% compared to static algorithm combinations and other cross-layer adaptation methods. This demonstrates the effectiveness of the framework in managing and optimizing resource allocation in a flexible and adaptable manner.

关键词： Resource management Massive MIMO Heuristic algorithms Quality of service Antennas Cross layer design Precoding Mobile network resource allocation QoS deep reinforcement learning automated algorithm selection massive MIMO

来源：评论

学校读者我要写书评

暂无评论

Deep Learning as a Competitive Feature-Free Approach for automated algorithm selection on the Traveling Salesperson Problem 16th

Deep Learning as a Competitive Feature-Free Approach for Aut...

引用

16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Seiler, Moritz Pohl, Janina Bossek, Jakob Kerschke, Pascal Trautmann, Heike Univ Munster Stat & Optimizat Grp Munster Germany Univ Adelaide Optimisat & Logist Adelaide SA Australia

ISBN: (纸本)9783030581114;9783030581121

In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with 1 000 nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.

关键词： automated algorithm selection Traveling Salesperson Problem Feature-based approaches Deep learning

来源：评论

学校读者我要写书评

暂无评论

On the Potential of Normalized TSP Features for automated algorithm selection 21

On the Potential of Normalized TSP Features for Automated Al...

引用

16th ACM/SIGEVO Conference on Foundations of Genetic algorithms (FOGA)

作者： Heins, Jonathan Bossek, Jakob Pohl, Janina Seiler, Moritz Trautmann, Heike Kerschke, Pascal Univ Munster Stat & Optimizat Munster Germany Tech Univ Dresden Big Data Analyt Transportat Dresden Germany

ISBN: (纸本)9781450383523

Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.

关键词： automated algorithm selection traveling salesperson problem (TSP) normalization instance features graph theory

来源：评论

学校读者我要写书评

暂无评论

Anytime Behavior of Inexact TSP Solvers and Perspectives for automated algorithm selection

Anytime Behavior of Inexact TSP Solvers and Perspectives for...

引用

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Bossek, Jakob Kerschke, Pascal Trautmann, Heike Univ Adelaide Optimisat & Logist Adelaide SA Australia Univ Munster Informat Syst & Stat Munster Germany

ISBN: (纸本)9781728169293

The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.

关键词： anytime behavior traveling salesperson problem automated algorithm selection performance assessment hybridization

来源：评论

学校读者我要写书评

暂无评论

Darwin & Goliath: A White-Label Recommender-System As-a-Service with automated algorithm-selection 19

Darwin & Goliath: A White-Label Recommender-System As-a-Serv...

引用

13th ACM Conference on Recommender Systems (RECSYS)

作者： Beel, Joeran Griffin, Alan O'Shea, Conor Trinity Coll Dublin ADAPT Ctr Sch Comp Sci & Stat Dublin Ireland

ISBN: (纸本)9781450362436

Recommendations-as-a-Service (RaaS) ease the process for small and medium-sized enterprises (SMEs) to offer product recommendations to their customers. Current RaaS, however, suffer from a one-size-fits-all concept, i.e. they apply the same recommendation algorithm for all SMEs. We introduce Darwin & Goliath, a RaaS that features multiple recommendation frameworks (Apache Lucene, TensorFlow, ...), and identifies the ideal algorithm for each SME automatically. Darwin & Goliath further offers per-instance algorithm selection and a white label feature that allows SMEs to offer a RaaS under their own brand. Since November 2018, Darwin & Goliath has delivered more than 1m recommendations with a CTR = 0.5%.

关键词： Recommendation API automated algorithm selection

来源：评论

学校读者我要写书评

暂无评论

Hybridizing Target- and SHAP-Encoded Features for algorithm selection in Mixed-Variable Black-Box Optimization 18th

Hybridizing Target- and SHAP-Encoded Features for Algorithm ...

引用

18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Dietrich, Konstantin Prager, Raphael Patrick Doerr, Carola Trautmann, Heike Tech Univ Dresden Big Data Analyt Transportat Dresden Germany ScaDS AI Dresden Germany Univ Munster Data Sci Stat & Optimizat Munster Germany Sorbonne Univ CNRS LIP6 Paris France Paderborn Univ Machine Learning & Optimisat Paderborn Germany Univ Twente Data Management & Biometr Grp Enschede Netherlands

ISBN: (纸本)9783031700675;9783031700682

Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm design, and applications such as automated algorithm selection and configuration. Until recently, however, ELA was limited to search spaces with either continuous or discrete variables, neglecting problems with mixed variable types. This gap was addressed in a recent study that uses an approach based on target-encoding to compute exploratory landscape features for mixed-variable problems. In this work, we investigate an alternative encoding scheme based on SHAP values. While these features do not lead to better results in the algorithm selection setting considered in previous work, the two different encoding mechanisms exhibit complementary performance. Combining both feature sets into a hybrid approach outperforms each encoding mechanism individually. Finally, we experiment with two different ways of meta-selecting between the two feature sets. Both approaches are capable of taking advantage of the performance complementarity of the models trained on target-encoded and SHAP-encoded feature sets, respectively.

关键词： Mixed-Variable Optimisation SHAP automated algorithm selection

来源：评论

学校读者我要写书评

暂无评论

algorithm selection for Combinatorial Packing Problems

Algorithm Selection for Combinatorial Packing Problems

引用

IEEE Congress on Evolutionary Computation (CEC)

作者： Sitaru, Ioana Raschip, Madalina Alexandru Ioan Cuza Univ Fac Comp Sci Iasi Romania

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

This paper describes a way of building an algorithm selection system for the two-dimensional rectangle packing problem. A list of features was defined, and five heuristics and metaheuristics were selected as solvers. The first step involves the extraction of features from the input problem instances. Using those computed features, machine learning regressors will predict the performance of each solver on the instance and, therefore, find the most fitted algorithm for it. Both the predicted execution time and the expected quality of the solution were used as performance metrics for the selection step. The experimental results showed that the built system has a high accuracy in predicting the best algorithm based on the runtime execution and the solution quality. Moreover, the system achieves these results with much lower computational resources than running the actual solvers of the problem.

关键词： automated algorithm selection strip packing runtime prediction performance prediction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：