@article{48877,
  abstract     = {{OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online.}},
  author       = {{Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd}},
  issn         = {{0943-4062}},
  journal      = {{Computational Statistics}},
  keywords     = {{Databases, Machine learning, R, Reproducible research}},
  number       = {{3}},
  pages        = {{977–991}},
  title        = {{{OpenML: An R Package to Connect to the Machine Learning Platform OpenML}}},
  doi          = {{10.1007/s00180-017-0742-2}},
  volume       = {{34}},
  year         = {{2019}},
}

@inproceedings{46339,
  abstract     = {{Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.}},
  author       = {{Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}},
  booktitle    = {{Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)}},
  editor       = {{Friedrich, Tobias and Doerr, Carola and Arnold, Dirk}},
  pages        = {{58–71}},
  title        = {{{Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}}},
  doi          = {{10.1145/3299904.3340307}},
  year         = {{2019}},
}

@inproceedings{46338,
  abstract     = {{We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{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}},
  isbn         = {{978-3-030-12597-4}},
  pages        = {{516–528}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}}},
  doi          = {{10.1007/978-3-030-12598-1_41}},
  volume       = {{11411}},
  year         = {{2019}},
}

@inproceedings{46337,
  abstract     = {{A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques.}},
  author       = {{Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Learning and Intelligent Optimization}},
  editor       = {{Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}},
  isbn         = {{978-3-030-05347-5}},
  pages        = {{215–219}},
  publisher    = {{Springer}},
  title        = {{{Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}}},
  volume       = {{11353}},
  year         = {{2019}},
}

@inproceedings{46350,
  abstract     = {{The ubiquity of WiFi access points and the sharp increase in WiFi-enabled devices carried by humans have paved the way for WiFi-based indoor positioning and location analysis. Locating people in indoor environments has numerous applications in robotics, crowd control, indoor facility optimization, and automated environment mapping. However, existing WiFi-based positioning systems suffer from two major problems: (1) their accuracy and precision is limited due to inherent noise induced by indoor obstacles, and (2) they only occasionally provide location estimates, namely when a WiFi-equipped device emits a signal. To mitigate these two issues, we propose a novel Gaussian process (GP) model for WiFi signal strength measurements. It allows for simultaneous smoothing (increasing accuracy and precision of estimators) and interpolation (enabling continuous sampling of location estimates). Furthermore, simple and efficient smoothing methods for location estimates are introduced to improve localization performance in real-time settings. Experiments are conducted on two data sets from a large real-world commercial indoor retail environment. Results demonstrate that our approach provides significant improvements in terms of precision and accuracy with respect to unfiltered data. Ultimately, the GP model realizes continuous location sampling with consistently high quality location estimates.}},
  author       = {{van Engelen, J.E. and van Lier, J.J. and Takes, F.W. and Trautmann, Heike}},
  booktitle    = {{Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)}},
  pages        = {{524–540}},
  publisher    = {{Springer}},
  title        = {{{Accurate WiFi based indoor positioning with continuous location sampling}}},
  year         = {{2018}},
}

@article{46351,
  abstract     = {{Clustering is an important field in data mining that aims to reveal hidden patterns in data sets. It is widely popular in marketing or medical applications and used to identify groups of similar objects. Clustering possibly unbounded and evolving data streams is of particular interest due to the widespread deployment of large and fast data sources such as sensors. The vast majority of stream clustering algorithms employ a two-phase approach where the stream is first summarized in an online phase. Upon request, an offline phase reclusters the aggregations into the final clusters. In this setup, the online component will idle and wait for the next observation in times where the stream is slow. This paper proposes a new stream clustering algorithm called evoStream which performs evolutionary optimization in the idle times of the online phase to incrementally build and refine the final clusters. Since the online phase would idle otherwise, our approach does not reduce the processing speed while effectively removing the computational overhead of the offline phase. In extensive experiments on real data streams we show that the proposed algorithm allows to output clusters of high quality at any time within the stream without the need for additional computational resources.}},
  author       = {{Carnein, Matthias and Trautmann, Heike}},
  journal      = {{Big Data Research}},
  pages        = {{101–111}},
  title        = {{{evoStream — Evolutionary Stream Clustering Utilizing Idle Times}}},
  doi          = {{10.1016/j.bdr.2018.05.005}},
  volume       = {{14}},
  year         = {{2018}},
}

@article{46353,
  abstract     = {{Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.}},
  author       = {{Li, L and Wang, Y and Trautmann, Heike and Jing, N and Emmerich, M}},
  journal      = {{Swarm and Evolutionary Computation}},
  pages        = {{196–215}},
  title        = {{{Multiobjective evolutionary algorithms based on target region preferences}}},
  doi          = {{10.1016/j.swevo.2018.02.006}},
  volume       = {{40}},
  year         = {{2018}},
}

@inproceedings{48839,
  abstract     = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-5618-3}},
  keywords     = {{combinatorial optimization, metaheuristics, multi-objective optimization, orienteering, transportation}},
  pages        = {{585–592}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Local Search Effects in Bi-Objective Orienteering}}},
  doi          = {{10.1145/3205455.3205548}},
  year         = {{2018}},
}

@inproceedings{48867,
  abstract     = {{Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g., a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.}},
  author       = {{Bossek, Jakob}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{evolutionary optimization, performance assessment, software-tools}},
  pages        = {{1350–1356}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr}}},
  doi          = {{10.1145/3205651.3208312}},
  year         = {{2018}},
}

@inproceedings{48885,
  abstract     = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}},
  author       = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{algorithm selection, optimization, performance measures, transportation, travelling salesperson problem}},
  pages        = {{1737–1744}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}},
  doi          = {{10.1145/3205651.3208233}},
  year         = {{2018}},
}

@book{48880,
  author       = {{Grimme, Christian and Bossek, Jakob}},
  isbn         = {{978-3-658-21150-9}},
  publisher    = {{Springer Vieweg}},
  title        = {{{Einführung in die Optimierung - Konzepte, Methoden und Anwendungen}}},
  doi          = {{10.1007/978-3-658-21151-6}},
  year         = {{2018}},
}

@article{48884,
  abstract     = {{The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.}},
  author       = {{Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}},
  issn         = {{1063-6560}},
  journal      = {{Evolutionary Computation}},
  keywords     = {{automated algorithm selection, machine learning., performance modeling, Travelling Salesperson Problem}},
  number       = {{4}},
  pages        = {{597–620}},
  title        = {{{Leveraging TSP Solver Complementarity through Machine Learning}}},
  doi          = {{10.1162/evco_a_00215}},
  volume       = {{26}},
  year         = {{2018}},
}

@article{48866,
  abstract     = {{Bossek, (2018). grapherator: A Modular Multi-Step Graph Generator. Journal of Open Source Software, 3(22), 528, https://doi.org/10.21105/joss.00528}},
  author       = {{Bossek, Jakob}},
  issn         = {{2475-9066}},
  journal      = {{Journal of Open Source Software}},
  number       = {{22}},
  pages        = {{528}},
  title        = {{{Grapherator: A Modular Multi-Step Graph Generator}}},
  doi          = {{10.21105/joss.00528}},
  volume       = {{3}},
  year         = {{2018}},
}

@inproceedings{46348,
  abstract     = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Guenter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-5618-3}},
  pages        = {{585–592}},
  publisher    = {{ACM}},
  title        = {{{Local Search Effects in Bi-Objective Orienteering}}},
  doi          = {{10.1145/3205455.3205548}},
  year         = {{2018}},
}

@article{46352,
  abstract     = {{The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.}},
  author       = {{Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}},
  journal      = {{Evolutionary Computation (ECJ)}},
  number       = {{4}},
  pages        = {{597–620}},
  title        = {{{Leveraging TSP Solver Complementarity through Machine Learning}}},
  doi          = {{10.1162/evco_a_00215}},
  volume       = {{26}},
  year         = {{2018}},
}

@inproceedings{46349,
  abstract     = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}},
  author       = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion}},
  isbn         = {{978-1-4503-5764-7/18/07}},
  pages        = {{1737–1744}},
  title        = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}},
  doi          = {{10.1145/3205651.3208233}},
  year         = {{2018}},
}

@inbook{46355,
  abstract     = {{In this chapter we present the adaptions of the recently proposed Directed Search method to the context of unconstrained parameter dependent multi-objective optimization problems (PMOPs). The new method, called 𝜆-DS, is capable of performing a movement both toward and along the solution set of a given differentiable PMOP. We first discuss the basic variants of the method that use gradient information and describe subsequently modifications that allow for a gradient free realization. Finally, we show that 𝜆-DS can be used to understand the behavior of stochastic local search within PMOPs to a certain extent which might be interesting for the development of future local search engines, or evolutionary strategies, for the treatment of such problems. We underline all our statements with several numerical results indicating the strength of the novel approach.}},
  author       = {{Adrián, Sosa Hernández V and Lara, A and Trautmann, Heike and Rudolph, G and Schütze, O}},
  booktitle    = {{NEO 15}},
  editor       = {{Schütze, O and Trujillo, L and Legrand, P and Maldonado, Y}},
  isbn         = {{978-3-319-44003-3}},
  pages        = {{281–330}},
  publisher    = {{Springer International Publishing}},
  title        = {{{The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems}}},
  doi          = {{10.1007/978-3-319-44003-3_12}},
  year         = {{2017}},
}

@inproceedings{46360,
  abstract     = {{Nowadays customers expect a seamless interaction with companies throughout all available communication channels. However, many companies rely on different software solutions to handle each channel, which leads to heterogeneous IT infrastructures and isolated data sources. Omni-Channel CRM is a holistic approach towards a unified view on the customer across all channels. This paper introduces three case studies which demonstrate challenges of omni-channel CRM and the value it can provide. The first case study shows how to integrate and visualise data from different sources which can support operational and strategic decision. In the second case study, a social media analysis approach is discussed which provides benefits by offering reports of service performance across channels. The third case study applies customer segmentation to an online fashion retailer in order to identify customer profiles.}},
  author       = {{Carnein, Matthias and Heuchert, Markus and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried and Becker, Jörg and Kraume, Karsten}},
  booktitle    = {{Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17)}},
  editor       = {{de Cesare, Sergio and Ulrich, Frank}},
  isbn         = {{978-3-319-70625-2}},
  pages        = {{69–78}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Towards Efficient and Informative Omni-Channel Customer Relationship Management}}},
  doi          = {{10.1007/978-3-319-70625-2_7}},
  volume       = {{10651}},
  year         = {{2017}},
}

@inproceedings{46361,
  abstract     = {{Until recently, customer service was exclusively provided over traditional channels. Cus- tomers could write an email or call a service center if they had questions or problems with a product or service. In recent times, this has changed dramatically as companies explore new channels to offer customer service. With the increasing popularity of social media, more companies thrive to provide customer service also over Facebook and Twitter. Companies aim to provide a better customer ex- perience by offering more convenient channels to contact a company. In addition, this unburdens traditional channels which are costly to maintain. This paper empirically evaluates the performance of customer service in social media by analysing a multitude of companies in the airline industry. We have collected several million customer service requests from Twitter and Facebook and auto- matically analyzed how efficient the service strategies of the respective companies are in terms of response rate and time.}},
  author       = {{Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried and Kraume, Karsten}},
  booktitle    = {{Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17)}},
  editor       = {{Ritter, Norbert and Schwarz, Holger and Klettke, Meike and Thor, Andreas and Kopp, Oliver and Bernhard, Matthias Wieland}},
  issn         = {{978-3-88579-660-2}},
  pages        = {{33–40}},
  publisher    = {{Gesellschaft für Informatik}},
  title        = {{{Customer Service in Social Media — An Empirical Study of the Airline Industry}}},
  volume       = {{P-266}},
  year         = {{2017}},
}

@inbook{46356,
  abstract     = {{Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preference-based multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Protégé. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.}},
  author       = {{Li, L and Yevseyeva, I and Basto-Fernandes, V and Trautmann, Heike and Jing, N and Emmerich, M}},
  booktitle    = {{Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings}},
  editor       = {{Trautmann, H and Rudolph, G and Klamroth, K and Schütze, O and Wiecek, M and Jin, Y and Grimme, C}},
  isbn         = {{978-3-319-54157-0}},
  pages        = {{406–421}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms}}},
  doi          = {{10.1007/978-3-319-54157-0_28}},
  year         = {{2017}},
}