@inproceedings{48869,
  abstract     = {{Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems that also involve dynamic and/or stochastic components in a systematic way in order to further increase their applicability to real-world problems. We investigate the node weighted traveling salesperson problem (W-TSP), which provides an abstraction of a wide range of weighted TSP problems, in dynamic settings. In the dynamic setting of the problem, items that have to be collected as part of a TSP tour change over time. We first present a dynamic setup for the dynamic W-TSP parameterized by different types of changes that are applied to the set of items to be collected when traversing the tour. Our first experimental investigations study the impact of such changes on resulting optimized tours in order to provide structural insights of optimization solutions. Afterwards, we investigate simple mutation-based evolutionary algorithms and study the impact of the mutation operators and the use of populations with dealing with the dynamic changes to the node weights of the problem.}},
  author       = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{9798400701191}},
  keywords     = {{dynamic optimization, evolutionary algorithms, re-optimization, weighted traveling salesperson problem}},
  pages        = {{248–256}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem}}},
  doi          = {{10.1145/3583131.3590384}},
  year         = {{2023}},
}

@article{53317,
  author       = {{Tao, Youshan and Winkler, Michael}},
  issn         = {{2163-2480}},
  journal      = {{Evolution Equations and Control Theory}},
  keywords     = {{Applied Mathematics, Control and Optimization, Modeling and Simulation}},
  number       = {{6}},
  pages        = {{1676--1687}},
  publisher    = {{American Institute of Mathematical Sciences (AIMS)}},
  title        = {{{Global smooth solutions in a three-dimensional cross-diffusive SIS epidemic model with saturated taxis at large densities}}},
  doi          = {{10.3934/eect.2023031}},
  volume       = {{12}},
  year         = {{2023}},
}

@inbook{30289,
  abstract     = {{This chapter presents a discussion of the concept of agency. Agency is understood as a multifaceted construct describing the idea that human beings make choices, act on these choices, and thereby exercise influence on their own lives as well as their environment. We argue that the concept is discussed from three different perspectives in the literature—transformational, dispositional, and relational—that are each related to learning and development in work contexts. These perspectives do not reflect incompatible positions but rather different aspects of the same phenomena. The chapter also offers an avenue of insight into empirical studies that employ agency as a central concept as well as discussions about concepts that closely overlap with ideas of human beings as agents of power and influence.}},
  author       = {{Goller, Michael and Paloniemi, Susanna}},
  booktitle    = {{Research Approaches on Workplace Learning}},
  isbn         = {{9783030895815}},
  issn         = {{2210-5549}},
  keywords     = {{Agency Workplace learning Professional development Proactivity Self-direction}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Agency: Taking Stock of Workplace Learning Research}}},
  doi          = {{10.1007/978-3-030-89582-2_1}},
  year         = {{2022}},
}

@inbook{30290,
  abstract     = {{The article explores the particular quality of changes introduced through the latest wave of digital transformation of workplaces. It has effects on workflow processes, on distribution of work and tasks, and the mode of distributing working tasks, e.g. through cyber-physical systems. Hence, the changes in work are manifold and require changes in vocational education and training as well as in workplace learning. These changes reveal new challenges for research on workplace learning. Finally, conclusions for future workplace learning research will be developed.}},
  author       = {{Harteis, Christian}},
  booktitle    = {{Research Approaches on Workplace Learning}},
  isbn         = {{9783030895815}},
  issn         = {{2210-5549}},
  keywords     = {{Digitalisation Self organisation Distribution of labour Automation}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Research on Workplace Learning in Times of Digitalisation}}},
  doi          = {{10.1007/978-3-030-89582-2_19}},
  year         = {{2022}},
}

@article{30861,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>We consider the problem of maximization of metabolite production in bacterial cells formulated as a dynamical optimal control problem (DOCP). According to Pontryagin’s maximum principle, optimal solutions are concatenations of singular and bang arcs and exhibit the chattering or <jats:italic>Fuller</jats:italic> phenomenon, which is problematic for applications. To avoid chattering, we introduce a reduced model which is still biologically relevant and retains the important structural features of the original problem. Using a combination of analytical and numerical methods, we show that the singular arc is dominant in the studied DOCPs and exhibits the <jats:italic>turnpike</jats:italic> property. This property is further used in order to design simple and realistic suboptimal control strategies.</jats:p>}},
  author       = {{Caillau, Jean-Baptiste and Djema, Walid and Gouzé, Jean-Luc and Maslovskaya, Sofya and Pomet, Jean-Baptiste}},
  issn         = {{0022-3239}},
  journal      = {{Journal of Optimization Theory and Applications}},
  keywords     = {{Applied Mathematics, Management Science and Operations Research, Control and Optimization}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Turnpike Property in Optimal Microbial Metabolite Production}}},
  doi          = {{10.1007/s10957-022-02023-0}},
  year         = {{2022}},
}

@inproceedings{29842,
  abstract     = {{To build successful software products, developers continuously have to discover what features the users really need. This discovery can be achieved with continuous experimentation, testing different software variants with distinct user groups, and deploying the superior variant for all users. However, existing approaches do not focus on explicit modeling of variants and experiments, which offers advantages such as traceability of decisions and combinability of experiments. Therefore, our vision is the provision of model-driven continuous experimentation, which provides the developer with a framework for structuring the experimentation process. For that, we introduce the overall concept, apply it to the experimentation on component-based software architectures and point out future research questions. In particular, we show the applicability by combining feature models for modeling the software variants, users, and experiments (i.e., model-driven) with MAPE-K for the adaptation (i.e., continuous experimentation) and implementing the concept based on the component-based Angular framework.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}},
  booktitle    = {{Proceedings of the 18th International Conference on Software Architecture Companion }},
  keywords     = {{continuous experimentation, model-driven, component-based software architectures, self-adaptation}},
  location     = {{Hawaii}},
  publisher    = {{IEEE}},
  title        = {{{Model-driven Continuous Experimentation on Component-based Software Architectures }}},
  doi          = {{10.1109/ICSA-C54293.2022.00011}},
  year         = {{2022}},
}

@article{35136,
  abstract     = {{Im Zentrum dieses Beitrags stehen Ergebnisse der Messung pädagogischer Kompetenzen Studierender der Theologie, die das Praxissemester in Deutschland absolviert haben. Das bildungswissenschaftliche Wissen, Kompetenzselbsteinschätzungen und ihre Entwicklung sowie die Einschätzung der im Praxissemester erreichten Ziele Studierender werden dabei unter Berücksichtigung der Ausrichtung des Lehramtsstudiums auf eine Schulform betrachtet. Um die Ergebnisse der Messung bildungswissenschaftlichen Wissens und die der Messung von Kompetenzselbsteinschätzungen zu kontextualisieren (N = 304), wird zuerst die Relevanz des (bildungswissenschaftlichen) Wissens als Ausgangspunkt des Könnens herausgearbeitet. Daran anschließend werden Befunde zur schulformspezifischen Professionalisierung resümiert. Anschließend werden Hypothesen hergeleitet, die Anlage der Studie sowie die Testinstrumente vorge- stellt, die Ergebnisse präsentiert und diskutiert. Die Ergebnisse zeigen wider Erwarten, dass sich weder das bildungswissenschaftliche Wissen, die Kompetenzselbsteinschätzungen und ihre Entwicklung noch die Einschätzung der im Praxissemester erreichten Ziele angehender Lehrkräfte in Abhängigkeit der Schulformen unterscheiden. Die Diskussion bezieht sich u.a. auf die Struktur der Lehramtsstudiengänge, die Denkfiguren zur Entwicklung von Können und die Konzeption der Messinstrumente.
}},
  author       = {{Caruso, Carina and Seifert, Andreas}},
  issn         = {{1018-1539}},
  journal      = {{Österreichische Religionspädagogische Forum}},
  keywords     = {{Bildungswissenschaftliches Wissen, Kompetenzmessung, Kompetenzselbsteinschätzung, Praxissemester, Professionalisierung / competence measurement, competence self-assessment, educational knowledge, internship, professionalization}},
  number       = {{1}},
  pages        = {{239--260}},
  publisher    = {{Universitätsbibliothek Graz}},
  title        = {{{ Inwiefern ist die Professionalisierung in Praxisphasen schulformspezifisch?}}},
  doi          = {{10.30:2022.1.14}},
  volume       = {{30}},
  year         = {{2022}},
}

@article{35137,
  abstract     = {{Im Zentrum dieses Beitrags stehen Ergebnisse der Messung pädagogischer Kompetenzen Studierender. Dabei werden sowohl das bildungswissenschaftliche Wissen als auch die Entwicklung der Kompe­tenzselbsteinschätzungen in den Bereichen Unterrichten, Erziehen, Beurteilen und Innovieren unter Berücksichtigung individueller Voraussetzungen (Alter, Geschlecht, Abiturnote, Bachelornote, Konfession) betrachtet. Um die Ergeb­nisse hinsichtlich ihrer Bedeutung für die Professionalisierung angehender Lehrkräfte diskutieren zu können, wird, den empirischen Erkenntnissen voranstehend, die Bedeutung von Wissen für berufliches Können herausgearbeitet. Daran anschließend werden Hypothesen hergeleitet, die Anlage der Studie sowie die Testinstrumente vorgestellt, die Ergebnisse präsentiert und diskutiert. Die Ergebnisse zeigen, dass die Abitur- und Bachelornote die Varianz hinsichtlich des pädagogischen Wissens aufklären, sich eine signifikante Entwicklung der Kompetenzselbsteinschätzungen angehender Lehrkräfte feststellen lässt, aber sich angehende Religionslehrkräfte kaum von anderen Studierenden unterscheiden. Die Diskussion nimmt u. a. Rückbezug auf die Denkfiguren zur Entwicklung berufli­chen Könnens und benennt Limitationen, die mit der Studie und Kompetenzmessungen verbunden sind. Daran schließt die Formulierung eines Ausblicks an. Der Beitrag zielt insbesondere darauf, repräsentative Ergebnisse der Kompetenzmessung zu präsentieren und dabei potenzielle Einflussfaktoren auf die studentische Kompetenzent­wicklung zu beleuchten. Ein dadurch angereichertes Konglomerat belastbarer Erkenntnisse zielt darauf, langfristig zur Ableitung lehrerbildungsdidaktischer Überlegungen herangezogen werden zu können, die die studentische Professionalisierung unterstützen.}},
  author       = {{Caruso, Carina and Seifert, Andreas}},
  issn         = {{2750 - 3941}},
  journal      = {{Religionspädagogische Beiträge. Journal for Religion in Education }},
  keywords     = {{Bildungswissenschaftliches Wissen, Kompetenzmessung, Kompetenzselbsteinschätzung, Praxissemester, Professionalisierung / competence measurement, competence self-assessment, educational knowledge, internship, professionalization}},
  number       = {{1}},
  pages        = {{3--15}},
  publisher    = {{University of Bamberg Press}},
  title        = {{{Pädagogische Kompetenz als Ausgangspunkt beruflichen Könnens!? Ergebnisse der Kompetenzmessung angehender Lehrkräfte unter Berücksichtigung individueller Voraussetzungen}}},
  doi          = {{10.20377/rpb-101}},
  volume       = {{45}},
  year         = {{2022}},
}

@inproceedings{48882,
  abstract     = {{In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.}},
  author       = {{Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Parallel Problem Solving from Nature (PPSN XVII)}},
  editor       = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}},
  isbn         = {{978-3-031-14714-2}},
  keywords     = {{Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric}},
  pages        = {{192–206}},
  publisher    = {{Springer International Publishing}},
  title        = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}},
  doi          = {{10.1007/978-3-031-14714-2_14}},
  year         = {{2022}},
}

@inproceedings{48896,
  abstract     = {{Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.}},
  author       = {{Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-9268-6}},
  keywords     = {{configuration, multi-modality, multi-objective optimization}},
  pages        = {{356–359}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}}},
  doi          = {{10.1145/3520304.3528998}},
  year         = {{2022}},
}

@article{35206,
  author       = {{Bonnard, Bernard and Rouot, Jérémy and Wembe Moafo, Boris Edgar}},
  issn         = {{2156-8472}},
  journal      = {{Mathematical Control and Related Fields}},
  keywords     = {{Applied Mathematics, Control and Optimization, General Medicine}},
  pages        = {{0--0}},
  publisher    = {{American Institute of Mathematical Sciences (AIMS)}},
  title        = {{{Accessibility properties of abnormal geodesics in optimal control illustrated by two case studies}}},
  doi          = {{10.3934/mcrf.2022052}},
  year         = {{2022}},
}

@inproceedings{40046,
  abstract     = {{Theoretical approaches to the transformation towards an inclusive educational system in Germany mostly agree on the involvement of developmental tasks in subject related research (Hinz, 2011). The common understanding of inclusion as a process geared towards equal participation of all children (Booth, 2012) requires a reflexive questioning of established values, attitudes and practices in order to develop inclusive subject related research, teacher training and teaching and learning (Pech & Schomaker, 2013). Among other things, this results in consequences for the design of pre- service teacher training. To a large extent, teacher education is driven by the promotion of central competencies, interests and self-efficacy (Baumert & Kunter, 2011). It aims towards the development and realisation of inclusive interdisciplinary science and social studies (‘Sachunterricht’) in primary education (Moser, 2018). In conjunction with largely acknowledged constructivist approaches to teaching and learning (Möller, 2001), the development of personality, the consideration of basic needs (Deci & Ryan, 1993) and promotion of individual potentials are repeatedly fundamentally represented in subject related and pedagogical considerations (Feuser, 1989; GDSU, 2013). Therefore, the aforementioned constructivist approach is connected to several certain key paradigms for teaching and learning processes (e.g., Vygotskij, 1978; Posner et al., 1982; van de Pol et al., 2010). In this regard, the nature of primary school students’ basic needs have empirically not been sufficiently studied yet. Theoretical frameworks from motivational psychology (Deci & Ryan, 1993) do not explicitly address how individual needs differ and how the diversity of needs can be included in joint-learning, multi-perspective technology education classes. The research project the present paper is part of aims to develop a research-based concept for the professionalisation of pre-service teachers in a seminar course. Therefore, the promotion of the pre- service teachers’ interests and self-efficacy expectations have been assessed in a pre-post research design with a control group visiting another course not related to technology education and inclusion. The present paper describes and discusses first results of the project and will give an outlook on subsequent developmental tasks.}},
  author       = {{Schröer, Franz and Tenberge, Claudia}},
  booktitle    = {{PATT39 - PATT on the Edge Technology, Innovation and Education}},
  editor       = {{Gill, David and Tuff, Jim and Kennedy, Thomas and Pendergast, Shawn and Jamil, Sana}},
  keywords     = {{Inclusion, basic needs, pre-service teacher training, interest, self-efficacy}},
  location     = {{St. John’s, Newfoundland and Labrador, Canada}},
  pages        = {{49--57}},
  title        = {{{How to enable pre-service teachers to design technological teaching and learning inclusively? – On the nature and consideration of basic needs in teacher training}}},
  year         = {{2022}},
}

@article{36083,
  author       = {{Constantiou, Ioanna and Mukkamala, Alivelu and Sjöklint, Mimmi and Trier, Matthias}},
  issn         = {{0960-085X}},
  journal      = {{European Journal of Information Systems}},
  keywords     = {{Library and Information Sciences, Information Systems, Self-Tracking, User Behaviour, Discontinuance}},
  pages        = {{1--21}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Engaging with self-tracking applications: how do users respond to their performance data?}}},
  doi          = {{10.1080/0960085x.2022.2081096}},
  year         = {{2022}},
}

@article{47961,
  abstract     = {{<jats:p>Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers.</jats:p>}},
  author       = {{Philipo, Godiana Hagile and Kakande, Josephine Nakato and Krauter, Stefan}},
  issn         = {{1996-1073}},
  journal      = {{Energies}},
  keywords     = {{Energy (miscellaneous), Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Control and Optimization, Engineering (miscellaneous), Building and Construction}},
  number       = {{14}},
  publisher    = {{MDPI AG}},
  title        = {{{Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping}}},
  doi          = {{10.3390/en15145215}},
  volume       = {{15}},
  year         = {{2022}},
}

@inproceedings{31066,
  abstract     = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }},
  author       = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}},
  keywords     = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}},
  location     = {{Casablanca, Morocco}},
  number       = {{12}},
  pages        = {{19--24}},
  title        = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}},
  doi          = {{https://doi.org/10.1016/j.ifacol.2022.07.282}},
  volume       = {{55}},
  year         = {{2022}},
}

@inproceedings{29803,
  abstract     = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and
power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in
the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture
this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the
bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian
optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of
bonding: the constraint is used to check one quality feature of the process and the use of batches leads to
more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding
process that provides very high quality bonds without using a physical model. We also show that the quality
of the Bayesian optimization based control outperforms random search as well as manual search by a user.
Using a simple prior knowledge model derived from data further improves the quality of the connection.
The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control
parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary,
Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward
control without full modeling of the underlying physical processes.}},
  author       = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}},
  booktitle    = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}},
  isbn         = {{978-989-758-549-4}},
  keywords     = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}},
  location     = {{Online}},
  pages        = {{383--394}},
  title        = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}},
  year         = {{2022}},
}

@article{34614,
  abstract     = {{Mit steigenden Optimierungsanforderungen an das Individuum wächst auch das indivi-
duelle Bedürfnis nach Kontrolle. Dieses kann u. a. durch self tracking-Technologien erfüllt werden.
Anhand von drei Fallbeispielen – der Personenwaage, dem Wearable und dem habit tracker – zeigt
dieser Aufsatz, wie sich medienbasierte Selbsttechnologien im historischen Verlauf intensiviert und
stärker in den Alltag integriert haben. Ein besonderer Fokus liegt dabei auf der Ambivalenz dieser
Medien: Ermöglichen sie auf der einen Seite zwar eine Selbstkontrolle und stellen so potenziell sta-
bilisierende Ressourcen für das Individuum dar, schaffen sie auf der anderen Seite auch neue
Anforderungen, die es zu erfüllen gilt.}},
  author       = {{Schloots, Franziska Margarete}},
  journal      = {{ffk Journal}},
  keywords     = {{self-tracking, Selbsttechnologien, Wearable, Bullet Journal, Personenwaage, Selbstvermessung}},
  number       = {{7}},
  pages        = {{74--91}},
  title        = {{{‚Understand what’s happening within‘. Selbstkontrolle mit Personenwaage, Wearable und habit tracker}}},
  doi          = {{10.25969/MEDIAREP/18238}},
  volume       = {{6}},
  year         = {{2022}},
}

@article{21004,
  abstract     = {{Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}},
  author       = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}},
  issn         = {{0162-8828}},
  journal      = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
  keywords     = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}},
  pages        = {{1--1}},
  title        = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}},
  doi          = {{10.1109/tpami.2021.3051276}},
  year         = {{2021}},
}

@article{21808,
  abstract     = {{Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge).

We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.}},
  author       = {{Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}},
  journal      = {{Transactions on Network and Service Management}},
  keywords     = {{network management, service management, coordination, reinforcement learning, self-learning, self-adaptation, multi-objective}},
  publisher    = {{IEEE}},
  title        = {{{Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}}},
  doi          = {{10.1109/TNSM.2021.3076503}},
  year         = {{2021}},
}

@techreport{33854,
  abstract     = {{Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice.

Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches.}},
  author       = {{Schneider, Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}},
  keywords     = {{mobility management, coordinated multipoint, CoMP, cell selection, resource management, reinforcement learning, multi agent, MARL, self-learning, self-adaptation, QoE}},
  title        = {{{DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning}}},
  year         = {{2021}},
}

