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Heike Trautmann

Bio: Heike Trautmann is an academic researcher from University of Münster. The author has contributed to research in topics: Multi-objective optimization & Optimization problem. The author has an hindex of 32, co-authored 143 publications receiving 3038 citations. Previous affiliations of Heike Trautmann include Karlsruhe Institute of Technology & University of Twente.


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

Proceedings ArticleDOI
12 Jul 2011
TL;DR: Interestingly, very few features are needed to separate the BBOB problem groups and also for relating a problem to high-level, expert designed features, paving the way for automatic algorithm selection.
Abstract: Exploratory Landscape Analysis subsumes a number of techniques employed to obtain knowledge about the properties of an unknown optimization problem, especially insofar as these properties are important for the performance of optimization algorithms. Where in a first attempt, one could rely on high-level features designed by experts, we approach the problem from a different angle here, namely by using relatively cheap low-level computer generated features. Interestingly, very few features are needed to separate the BBOB problem groups and also for relating a problem to high-level, expert designed features, paving the way for automatic algorithm selection.

300 citations

Proceedings ArticleDOI
07 Jul 2012
TL;DR: A comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way and the influence of the number and distribution of the weight vectors on the optimal distribution of μ solutions is analyzed.
Abstract: In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible The R2 and the Hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the R2 indicator exist In this paper, we thus perform a comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way The influence of the number and distribution of the weight vectors on the optimal distribution of μ solutions is analyzed Based on a comparative analysis, specific characteristics and differences of the R2 and HV indicator are presented

174 citations

Proceedings ArticleDOI
07 Jul 2012
TL;DR: The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis, and uses one-sided support vector regression to solve this learning problem.
Abstract: The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB'09/10 workshop.

143 citations

Journal ArticleDOI
TL;DR: Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features.
Abstract: Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.

133 citations


Cited by
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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