@inproceedings{46325, abstract = {{Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.}}, author = {{Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}}, booktitle = {{Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}}, isbn = {{978-3-030-43823-4}}, pages = {{137–143}}, title = {{{Towards Automated Configuration of Stream Clustering Algorithms}}}, doi = {{10.1007/978-3-030-43823-4_12}}, year = {{2020}}, } @inproceedings{46321, abstract = {{Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such automated programs, it is still unclear how technically sophisticated those bots are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo of social bot development as well as first insights into the overall skills of publicly available bot code.}}, author = {{Assenmacher, Dennis and Frischlich , Lena and Trautmann, Heike and Grimme, Christian and Adam, Lena}}, booktitle = {{Disinformation in open online media}}, editor = {{Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}}, pages = {{101–114}}, publisher = {{Springer}}, title = {{{Inside the tool set of automation: Free social bot code revisited}}}, year = {{2020}}, } @inproceedings{46326, abstract = {{Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.}}, author = {{Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}}, booktitle = {{Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)}}, pages = {{80–95}}, title = {{{confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms}}}, doi = {{10.1007/978-3-030-53552-0_10}}, year = {{2020}}, } @inproceedings{46327, abstract = {{In online media environments, nostalgia can be used as important ingredient of propaganda strategies, specifically, by creating societal pessimism. This work addresses the automated detection of nostalgic text as a first step towards automatically identifying nostalgia-based manipulation strategies. We compare the performance of standard machine learning approaches on this challenge and demonstrate the successful transfer of the best performing approach to real-world nostalgia detection in a case study.}}, author = {{Lena, Clever and Frischlich, Lena and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Disinformation in open online media}}, editor = {{Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}}, pages = {{48–58}}, title = {{{Automated detection of nostalgic text in the context of societal pessimism}}}, year = {{2020}}, } @inproceedings{46329, abstract = {{The past decade has been characterized by a strong increase in the use of social media and a continuous growth of public online discussion. With the failure of purely manual moderation, platform operators started searching for semi-automated solutions, where the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques is promising. However, this requires huge financial investments for algorithmic implementations, data collection, and model training, which only big players can afford. To support smaller or medium-sized media enterprises (SME), we developed an integrated comment moderation system as an IT platform. This platform acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating such a platform, however, requires a robust technology stack, integrated workflows and well-defined interfaces between all parties. In this paper, we develop and discuss a suitable IT architecture and present a prototypical implementation.}}, author = {{Riehle, Dennis M. and Niemann, Marco and Brunk, Jens and Assenmacher, Dennis and Trautmann, Heike and Becker, Jörg}}, booktitle = {{Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing}}, editor = {{Meiselwitz, Gabriele}}, isbn = {{978-3-030-49576-3}}, pages = {{71–86}}, publisher = {{Springer International Publishing}}, title = {{{Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool}}}, year = {{2020}}, } @article{46333, abstract = {{ Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots. }}, author = {{Assenmacher, Dennis and Clever, Lena and Frischlich, Lena and Quandt, Thorsten and Trautmann, Heike and Grimme, Christian}}, journal = {{Social Media + Society}}, number = {{3}}, pages = {{2056305120939264}}, title = {{{Demystifying Social Bots: On the Intelligence of Automated Social Media Actors}}}, doi = {{10.1177/2056305120939264}}, volume = {{6}}, year = {{2020}}, } @inproceedings{46332, abstract = {{Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.}}, author = {{Steinhoff, Vera and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{2445–2452}}, title = {{{Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent}}}, doi = {{10.1109/SSCI47803.2020.9308259}}, year = {{2020}}, } @inproceedings{46331, abstract = {{Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.}}, author = {{Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Proceedings of the International Joint Conference on Neural Networks (IJCNN)}}, pages = {{1–8}}, title = {{{Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries}}}, doi = {{10.1109/IJCNN48605.2020.9207338}}, year = {{2020}}, } @inproceedings{46330, abstract = {{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 1000 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.}}, author = {{Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)}}, editor = {{Bäck, Thomas and Preuss, Mike and Deutz, André and Wang, Hao and Doerr, Carola and Emmerich, Michael and Trautmann, Heike}}, pages = {{48–64}}, title = {{{Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}}}, doi = {{10.1007/978-3-030-58112-1_4}}, year = {{2020}}, } @article{46334, abstract = {{We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{1568-4946}}, journal = {{Applied Soft Computing}}, keywords = {{Algorithm selection, Multi-objective optimization, Performance measurement, Combinatorial optimization, Traveling Salesperson Problem}}, pages = {{105901}}, title = {{{A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}}}, doi = {{https://doi.org/10.1016/j.asoc.2019.105901}}, volume = {{88}}, year = {{2020}}, } @inproceedings{46322, abstract = {{We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n t and analyze all |D| nt combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.}}, author = {{Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}}, pages = {{1–8}}, title = {{{Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}}}, doi = {{10.1109/CEC48606.2020.9185778}}, year = {{2020}}, } @inproceedings{46324, abstract = {{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.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}}, pages = {{1–8}}, publisher = {{IEEE}}, title = {{{Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection}}}, year = {{2020}}, } @inproceedings{46323, abstract = {{In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.}}, author = {{Bossek, Jakob and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)}}, pages = {{166–174}}, publisher = {{ACM}}, title = {{{Dynamic Bi-Objective Routing of Multiple Vehicles}}}, year = {{2020}}, } @inproceedings{46343, abstract = {{This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets.}}, author = {{Grimme, Christian and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}}, editor = {{Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}}, pages = {{126–138}}, publisher = {{Springer}}, title = {{{Multimodality in Multi-Objective Optimization — More Boon than Bane?}}}, doi = {{10.1007/978-3-030-12598-1_11}}, volume = {{11411}}, year = {{2019}}, } @article{46345, 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 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.}}, author = {{Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}}, journal = {{Evolutionary Computation (ECJ)}}, number = {{1}}, pages = {{3–45}}, title = {{{Automated Algorithm Selection: Survey and Perspectives}}}, doi = {{10.1162/evco_a_00242}}, volume = {{27}}, year = {{2019}}, } @article{46344, abstract = {{Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.}}, author = {{Carnein, Matthias and Trautmann, Heike}}, journal = {{Business and Information Systems Engineering (BISE)}}, number = {{3}}, pages = {{277–297}}, title = {{{Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms}}}, volume = {{61}}, year = {{2019}}, } @inproceedings{46340, abstract = {{Recommender systems aim to provide personalized suggestions to customers which products to buy or services to consume. They can help to increase sales by helping customers discover new and relevant products. Traditionally, recommender systems use the purchase history of a customer, e.g., the purchased quantity or properties of the items. While this allows to build personalized recommendations, it is a very limited view of the problem. Nowadays, extensive information about customers and their personal preferences is available which goes far beyond their purchase behaviour. For example, customers reveal their preferences in social media, by their browsing habits and online search behaviour or their interest in specific newsletters. In this paper, we investigate how information from different sources and channels can be collected and incorporated into the recommendation process. We demonstrate this, based on a real-life case study of a retailer with several million transactions. We discuss how to employ a recommender system in this scenario, evaluate various recommendation strategies and describe how to incorporate information from different sources and channels, both internal and external. Our results show that the recommendations can be better tailored to the personal preferences of customers.}}, author = {{Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}}, booktitle = {{Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}}, pages = {{65–74}}, title = {{{A Recommender System Based on Omni-Channel Customer Data}}}, year = {{2019}}, } @inproceedings{46341, abstract = {{Customer Segmentation aims to identify groups of customers that share similar interest or behaviour. It is an essential tool in marketing and can be used to target customer segments with tailored marketing strategies. Customer segmentation is often based on clustering techniques. This analysis is typically performed as a snapshot analysis where segments are identified at a specific point in time. However, this ignores the fact that customer segments are highly volatile and segments change over time. Once segments change, the entire analysis needs to be repeated and strategies adapted. In this paper we explore stream clustering as a tool to alleviate this problem. We propose a new stream clustering algorithm which allows to identify and track customer segments over time. The biggest challenge is that customer segmentation often relies on the transaction history of a customer. Since this data changes over time, it is necessary to update customers which have already been incorporated into the clustering. We show how to perform this step incrementally, without the need for periodic re-computations. As a result, customer segmentation can be performed continuously, faster and is more scalable. We demonstrate the performance of our algorithm using a large real-life case study.}}, author = {{Carnein, Matthias and Trautmann, Heike}}, booktitle = {{Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)}}, pages = {{280–292}}, title = {{{Customer Segmentation Based on Transactional Data Using Stream Clustering}}}, year = {{2019}}, } @inproceedings{46342, abstract = {{There is a range of phenomena in continuous, global multi-objective optimization, that cannot occur in single-objective optimization. For instance, in some multi-objective optimization problems it is possible to follow continuous paths of gradients of straightforward weighted scalarization functions, starting from locally efficient solutions, in order to reach globally Pareto optimal solutions. This paper seeks to better characterize multimodal multi-objective landscapes and to better understand the transitions from local optima to global optima in simple, path-oriented search procedures.}}, author = {{Grimme, Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and Deutz, André H and Trautmann, Heike}}, booktitle = {{AIP Conference Proceedings}}, pages = {{020052--1--020052--4}}, publisher = {{AIP Publishing}}, title = {{{Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization}}}, doi = {{10.1063/1.5090019}}, year = {{2019}}, } @inbook{46336, 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.}}, author = {{Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Applications in Statistical Computing}}, editor = {{Bauer, Nadja and Ickstadt, Katja and Lübke, Karsten and Szepannek, Gero and Trautmann, Heike and Vichi, Maurizio}}, pages = {{93–123}}, publisher = {{Springer}}, title = {{{Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco}}}, doi = {{10.1007/978-3-030-25147-5_7}}, year = {{2019}}, }