@article{46308, abstract = {{Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems based on first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective domain and subsequently exploiting local structures of the resulting landscapes. Our study particularly focuses on the sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization of the artificial second objective, as well as (2) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Nelder–Mead local search as optimizer in the respective module and compare the performance of the resulting hybrid local search to its original single-objective counterpart. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Hence, combined with more sophisticated local search and metaheuristics, this may help solve highly multimodal optimization problems in the future.}}, author = {{Aspar, Pelin and Steinhoff, Vera and Schäpermeier, Lennart and Kerschke, Pascal and Trautmann, Heike and Grimme, Christian}}, journal = {{Natural Computing}}, pages = {{1–15}}, title = {{{The objective that freed me: a multi-objective local search approach for continuous single-objective optimization}}}, doi = {{10.1007/s11047-022-09919-w}}, volume = {{1}}, year = {{2022}}, } @inproceedings{48861, abstract = {{Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-9237-2}}, keywords = {{instance features, instance generation, quality diversity, TSP}}, pages = {{186–194}}, publisher = {{Association for Computing Machinery}}, title = {{{Exploring the Feature Space of TSP Instances Using Quality Diversity}}}, doi = {{10.1145/3512290.3528851}}, year = {{2022}}, } @inproceedings{48868, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-9268-6}}, pages = {{824–842}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}}}, doi = {{10.1145/3520304.3533626}}, 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{48894, abstract = {{Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.}}, author = {{Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}}, 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 Tu\v sar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Co-evolutionary algorithms, Evolutionary diversity optimisation, Quality diversity, Traveling thief problem}}, pages = {{237–249}}, publisher = {{Springer International Publishing}}, title = {{{Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}}}, doi = {{10.1007/978-3-031-14714-2_17}}, year = {{2022}}, } @article{48878, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{2076-3417}}, journal = {{Applied Sciences}}, keywords = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}}, number = {{18}}, pages = {{9094}}, publisher = {{{Multidisciplinary Digital Publishing Institute}}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, 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{52532, author = {{Rodrigues, Agatha S. and Kerschke, Pascal and Pereira, Carlos Alberto De Bragança and Trautmann, Heike and Wagner, Carolin and Hellingrath, Bernd and Polpo, Adriano}}, journal = {{Comput. Stat.}}, number = {{1}}, pages = {{355–379}}, title = {{{Estimation of component reliability from superposed renewal processes by means of latent variables}}}, doi = {{10.1007/S00180-021-01124-0}}, volume = {{37}}, year = {{2022}}, } @inproceedings{46307, abstract = {{Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.}}, author = {{Seiler, Moritz and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{9781450392372}}, pages = {{657–665}}, publisher = {{Association for Computing Machinery}}, title = {{{A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes}}}, doi = {{10.1145/3512290.3528834}}, year = {{2022}}, } @inproceedings{46304, abstract = {{In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.}}, author = {{Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, 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 Tušar, Tea}}, isbn = {{978-3-031-14714-2}}, pages = {{3–17}}, publisher = {{Springer International Publishing}}, title = {{{Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods}}}, doi = {{10.1007/978-3-031-14714-2_1}}, year = {{2022}}, } @inproceedings{46303, abstract = {{Social media platforms are essential for information sharing and, thus, prone to coordinated dis- and misinformation campaigns. Nevertheless, research in this area is hampered by strict data sharing regulations imposed by the platforms, resulting in a lack of benchmark data. Previous work focused on circumventing these rules by either pseudonymizing the data or sharing fragments. In this work, we will address the benchmarking crisis by presenting a methodology that can be used to create artificial campaigns out of original campaign building blocks. We conduct a proof-of-concept study using the freely available generative language model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly be adapted to an underlying social media stream and evade state-of-the-art campaign detection approaches based on stream clustering. Thus, we not only provide a framework for artificial benchmark generation but also demonstrate the possible adversarial nature of such benchmarks for challenging and advancing current campaign detection methods.}}, author = {{Pohl, Janina Susanne and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)}}, editor = {{the Advancement of Artificial Intelligence (AAAI) Association, for}}, pages = {{1–10}}, publisher = {{AAAI Press}}, title = {{{Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches}}}, doi = {{10.36190/2022.91}}, year = {{2022}}, } @article{46309, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, journal = {{Applied Sciences}}, number = {{8}}, pages = {{1–44}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, } @inproceedings{46302, author = {{Heins, J and Rook, J and Schäpermeier, L and Kerschke, P and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from Nature — PPSN XVII}}, editor = {{Rudolph, G and Kononova, AV and Aguirre, H and Kerschke, P and Ochoa, G and Tušar, T}}, isbn = {{978-3-031-14714-2}}, pages = {{192–206}}, publisher = {{Springer International Publishing}}, title = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}}, year = {{2022}}, } @inproceedings{46305, 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, J and Trautmann, Heike and Bossek, Jakob and Grimme, C}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, editor = {{Fieldsend, J and Wagner, M.}}, isbn = {{9781450392686}}, 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{46318, abstract = {{Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.}}, author = {{Grimme, Christian and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André H. and Wang, Hao and Emmerich, Michael}}, issn = {{0305-0548}}, journal = {{Computers & Operations Research}}, keywords = {{Multimodal optimization, Multi-objective continuous optimization, Landscape analysis, Visualization, Benchmarking, Theory, Algorithms}}, pages = {{105489}}, title = {{{Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization}}}, doi = {{https://doi.org/10.1016/j.cor.2021.105489}}, volume = {{136}}, year = {{2021}}, } @inproceedings{46311, abstract = {{In this work we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems by first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective space, and subsequently exploiting local structures and ridges of the resulting landscapes. Our study particularly focusses on the sensitivity of this multiobjectivization approach w.r.t. (i) the parametrization of the artificial second objective, as well as (ii) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient and Nelder-Mead local search (as optimizers in the respective module) and compare the performance of the resulting hybrid local search to their original single-objective counterparts. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Combined with more sophisticated local search and metaheuristics this may help in solving highly multimodal optimization problems in future.}}, author = {{Aspar, Pelin and Kerschke, Pascal and Steinhoff, Vera and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings}}, editor = {{et al. Ishibuchi, H.}}, pages = {{311–322}}, publisher = {{Springer}}, title = {{{Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization}}}, doi = {{10.1007/978-3-030-72062-9_25}}, year = {{2021}}, } @article{46317, abstract = {{One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity themes: Crime & conflict, Jobs, Attention, and Wellbeing. The outcomes of the workshop discussions identify three attributes of humanity that need preservation: a critique of the design and application of AI, and the intelligent machines it can create; human involvement in the loop of intelligent machine decision-making processes; and the ability to interpret and explain intelligent machine decision-making processes. The article provides an agenda for future AI and humanity research.}}, author = {{Coombs, Crispin and Stacey, Patrick and Kawalek, Peter and Simeonova, Boyka and Becker, Jörg and Bergener, Katrin and Carvalho, João Álvaro and Fantinato, Marcelo and Garmann-Johnsen, Niels F. and Grimme, Christian and Stein, Armin and Trautmann, Heike}}, journal = {{International Journal of Information Management}}, title = {{{What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective}}}, doi = {{10.1016/j.ijinfomgt.2021.102311}}, volume = {{58}}, year = {{2021}}, } @inproceedings{48853, abstract = {{In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, knapsack problem, tailored operators}}, pages = {{556–564}}, publisher = {{Association for Computing Machinery}}, title = {{{Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}}}, doi = {{10.1145/3449639.3459364}}, year = {{2021}}, } @inproceedings{48855, abstract = {{Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical knapsack problem. We present an algorithm to count exactly the number of optima to a zero-one knapsack problem instance. In addition, we show how to efficiently sample uniformly at random from the set of all global optima. In our experimental study, we investigate how the number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Learning and Intelligent Optimization}}, isbn = {{978-3-030-92120-0}}, keywords = {{Dynamic programming, Exact counting, Sampling, Zero-one knapsack problem}}, pages = {{40–54}}, publisher = {{Springer-Verlag}}, title = {{{Exact Counting and~Sampling of Optima for the Knapsack Problem}}}, doi = {{10.1007/978-3-030-92121-7_4}}, year = {{2021}}, } @inproceedings{48860, abstract = {{In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, minimum spanning tree, runtime analysis}}, pages = {{198–206}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}}}, doi = {{10.1145/3449639.3459363}}, year = {{2021}}, }