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Pascal Kerschke

Bio: Pascal Kerschke is an academic researcher from University of Münster. The author has contributed to research in topics: Optimization problem & Computer science. The author has an hindex of 17, co-authored 63 publications receiving 1077 citations. Previous affiliations of Pascal Kerschke include Dresden University of Technology.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection.
Abstract: 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 pronou...

304 citations

Journal ArticleDOI
TL;DR: In this article, the authors introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature, and demonstrate the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

196 citations

Journal ArticleDOI
TL;DR: In this paper, an algorithm selection model for continuous black-box optimization problems is presented, based on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications.
Abstract: 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.

120 citations

Book ChapterDOI
TL;DR: Flacco is introduced, an R-package for feature-based landscape analysis of continuous and constrained optimization problems that offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform—even within a single package.
Abstract: Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called black-box problems, and function evaluations are considered to be expensive. In case of continuous single-objective optimization problems, exploratory landscape analysis (ELA), a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself, is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been—if at all—spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related algorithm selection problem (ASP), it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform—even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package’s built-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI). It facilitates the usage of the package—especially for people who are not familiar with R—and thus makes flacco a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems.

87 citations

Proceedings ArticleDOI
11 Jul 2015
TL;DR: Specific features for detecting funnel structures are introduced and combined with the existing ones in order to classify optimization problems regarding the funnel property and the effectiveness of the approach is shown.
Abstract: In single-objective optimization different optimization strategies exist depending on the structure and characteristics of the underlying problem. In particular, the presence of so-called funnels in multimodal problems offers the possibility of applying techniques exploiting the global structure of the function. The recently proposed Exploratory Landscape Analysis approach automatically identifies problem characteristics based on a moderately small initial sample of the objective function and proved to be effective for algorithm selection problems in continuous black-box optimization. In this paper, specific features for detecting funnel structures are introduced and combined with the existing ones in order to classify optimization problems regarding the funnel property. The effectiveness of the approach is shown by experiments on specifically generated test instances and validation experiments on standard benchmark problems.

65 citations


Cited by
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Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

7,116 citations

01 Jan 2012

3,692 citations