[{"_id":"46317","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}],"type":"journal_article","publication":"International Journal of Information Management","abstract":[{"text":"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.","lang":"eng"}],"status":"public","date_updated":"2023-10-16T12:58:02Z","date_created":"2023-08-04T07:27:14Z","author":[{"first_name":"Crispin","full_name":"Coombs, Crispin","last_name":"Coombs"},{"first_name":"Patrick","full_name":"Stacey, Patrick","last_name":"Stacey"},{"last_name":"Kawalek","full_name":"Kawalek, Peter","first_name":"Peter"},{"full_name":"Simeonova, Boyka","last_name":"Simeonova","first_name":"Boyka"},{"full_name":"Becker, Jörg","last_name":"Becker","first_name":"Jörg"},{"full_name":"Bergener, Katrin","last_name":"Bergener","first_name":"Katrin"},{"first_name":"João Álvaro","last_name":"Carvalho","full_name":"Carvalho, João Álvaro"},{"last_name":"Fantinato","full_name":"Fantinato, Marcelo","first_name":"Marcelo"},{"full_name":"Garmann-Johnsen, Niels F.","last_name":"Garmann-Johnsen","first_name":"Niels F."},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"full_name":"Stein, Armin","last_name":"Stein","first_name":"Armin"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"volume":58,"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","year":"2021","citation":{"chicago":"Coombs, Crispin, Patrick Stacey, Peter Kawalek, Boyka Simeonova, Jörg Becker, Katrin Bergener, João Álvaro Carvalho, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” <i>International Journal of Information Management</i> 58 (2021). <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">https://doi.org/10.1016/j.ijinfomgt.2021.102311</a>.","ieee":"C. Coombs <i>et al.</i>, “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective,” <i>International Journal of Information Management</i>, vol. 58, 2021, doi: <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>.","ama":"Coombs C, Stacey P, Kawalek P, et al. What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. <i>International Journal of Information Management</i>. 2021;58. doi:<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>","bibtex":"@article{Coombs_Stacey_Kawalek_Simeonova_Becker_Bergener_Carvalho_Fantinato_Garmann-Johnsen_Grimme_et al._2021, title={What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective}, volume={58}, DOI={<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>}, journal={International Journal of Information Management}, 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 et al.}, year={2021} }","mla":"Coombs, Crispin, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” <i>International Journal of Information Management</i>, vol. 58, 2021, doi:<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>.","short":"C. Coombs, P. Stacey, P. Kawalek, B. Simeonova, J. Becker, K. Bergener, J.Á. Carvalho, M. Fantinato, N.F. Garmann-Johnsen, C. Grimme, A. Stein, H. Trautmann, International Journal of Information Management 58 (2021).","apa":"Coombs, C., Stacey, P., Kawalek, P., Simeonova, B., Becker, J., Bergener, K., Carvalho, J. Á., Fantinato, M., Garmann-Johnsen, N. F., Grimme, C., Stein, A., &#38; Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. <i>International Journal of Information Management</i>, <i>58</i>. <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">https://doi.org/10.1016/j.ijinfomgt.2021.102311</a>"},"intvolume":"        58"},{"publication_identifier":{"isbn":["978-1-4503-8350-9"]},"publication_status":"published","place":"New York, NY, USA","page":"556–564","citation":{"apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 556–564. <a href=\"https://doi.org/10.1145/3449639.3459364\">https://doi.org/10.1145/3449639.3459364</a>","mla":"Bossek, Jakob, et al. “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 556–564, doi:<a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>.","bibtex":"@inproceedings{Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, series={GECCO ’21}, title={Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={556–564}, collection={GECCO ’21} }","short":"J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 556–564.","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 556–564, doi: <a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>.","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 556–564. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459364\">https://doi.org/10.1145/3449639.3459364</a>.","ama":"Bossek J, Neumann A, Neumann F. Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’21. Association for Computing Machinery; 2021:556–564. doi:<a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>"},"date_updated":"2023-12-13T10:45:22Z","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"doi":"10.1145/3449639.3459364","type":"conference","status":"public","_id":"48853","department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO ’21","extern":"1","year":"2021","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:54Z","title":"Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"lang":"eng","text":"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."}],"keyword":["evolutionary algorithms","evolutionary diversity optimization","knapsack problem","tailored operators"],"language":[{"iso":"eng"}]},{"keyword":["Dynamic programming","Exact counting","Sampling","Zero-one knapsack problem"],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"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."}],"publication":"Learning and Intelligent Optimization","title":"Exact Counting and~Sampling of Optima for the Knapsack Problem","publisher":"Springer-Verlag","date_created":"2023-11-14T15:58:54Z","year":"2021","extern":"1","_id":"48855","user_id":"102979","department":[{"_id":"819"}],"status":"public","type":"conference","doi":"10.1007/978-3-030-92121-7_4","date_updated":"2023-12-13T10:45:14Z","author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"last_name":"Neumann","full_name":"Neumann, Aneta","first_name":"Aneta"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"place":"Berlin, Heidelberg","citation":{"apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Exact Counting and~Sampling of Optima for the Knapsack Problem. <i>Learning and Intelligent Optimization</i>, 40–54. <a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">https://doi.org/10.1007/978-3-030-92121-7_4</a>","short":"J. Bossek, A. Neumann, F. Neumann, in: Learning and Intelligent Optimization, Springer-Verlag, Berlin, Heidelberg, 2021, pp. 40–54.","mla":"Bossek, Jakob, et al. “Exact Counting And~Sampling of Optima for the Knapsack Problem.” <i>Learning and Intelligent Optimization</i>, Springer-Verlag, 2021, pp. 40–54, doi:<a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>.","bibtex":"@inproceedings{Bossek_Neumann_Neumann_2021, place={Berlin, Heidelberg}, title={Exact Counting and~Sampling of Optima for the Knapsack Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer-Verlag}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={40–54} }","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Exact Counting And~Sampling of Optima for the Knapsack Problem.” In <i>Learning and Intelligent Optimization</i>, 40–54. Berlin, Heidelberg: Springer-Verlag, 2021. <a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">https://doi.org/10.1007/978-3-030-92121-7_4</a>.","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Exact Counting and~Sampling of Optima for the Knapsack Problem,” in <i>Learning and Intelligent Optimization</i>, 2021, pp. 40–54, doi: <a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>.","ama":"Bossek J, Neumann A, Neumann F. Exact Counting and~Sampling of Optima for the Knapsack Problem. In: <i>Learning and Intelligent Optimization</i>. Springer-Verlag; 2021:40–54. doi:<a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>"},"page":"40–54","publication_status":"published","publication_identifier":{"isbn":["978-3-030-92120-0"]}},{"citation":{"ama":"Bossek J, Neumann F. Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’21. Association for Computing Machinery; 2021:198–206. doi:<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>","ieee":"J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 198–206, doi: <a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>.","chicago":"Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 198–206. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459363\">https://doi.org/10.1145/3449639.3459363</a>.","short":"J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 198–206.","bibtex":"@inproceedings{Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO ’21}, title={Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank}, year={2021}, pages={198–206}, collection={GECCO ’21} }","mla":"Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 198–206, doi:<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>.","apa":"Bossek, J., &#38; Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 198–206. <a href=\"https://doi.org/10.1145/3449639.3459363\">https://doi.org/10.1145/3449639.3459363</a>"},"page":"198–206","place":"New York, NY, USA","year":"2021","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-8350-9"]},"doi":"10.1145/3449639.3459363","title":"Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem","author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"date_created":"2023-11-14T15:58:55Z","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:45:37Z","status":"public","abstract":[{"lang":"eng","text":"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."}],"type":"conference","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","language":[{"iso":"eng"}],"extern":"1","keyword":["evolutionary algorithms","evolutionary diversity optimization","minimum spanning tree","runtime analysis"],"series_title":"GECCO ’21","user_id":"102979","department":[{"_id":"819"}],"_id":"48860"},{"abstract":[{"lang":"eng","text":"Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT {$\\cup$} S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT. We show that the answer to our question depends on the number and placement of search points in S. For all black-box algorithms and all fitness functions we show that, if additional optima are placed randomly, even an exponential number of optima has a negligible effect on the expected optimisation time. Considering Hamming balls around all global optima gives an easier target for some algorithms and functions and can shift the phase transition with respect to offspring population sizes in the (1,{$\\lambda$}) EA on One-Max. Finally, on functions where search trajectories typically join in a single search point, turning one search point into an optimum drastically reduces the expected optimisation time."}],"status":"public","publication":"Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","type":"book_chapter","keyword":["evolutionary algorithms","pseudo-boolean functions","runtime analysis","theory"],"language":[{"iso":"eng"}],"extern":"1","_id":"48862","department":[{"_id":"819"}],"user_id":"102979","place":"New York, NY, USA","year":"2021","page":"1–11","citation":{"ama":"Bossek J, Sudholt D. Do Additional Optima Speed up Evolutionary Algorithms? In: <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–11.","ieee":"J. Bossek and D. Sudholt, “Do Additional Optima Speed up Evolutionary Algorithms?,” in <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.","chicago":"Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary Algorithms?” In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–11. New York, NY, USA: Association for Computing Machinery, 2021.","apa":"Bossek, J., &#38; Sudholt, D. (2021). Do Additional Optima Speed up Evolutionary Algorithms? In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–11). Association for Computing Machinery.","short":"J. Bossek, D. Sudholt, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–11.","mla":"Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary Algorithms?” <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–11.","bibtex":"@inbook{Bossek_Sudholt_2021, place={New York, NY, USA}, title={Do Additional Optima Speed up Evolutionary Algorithms?}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Sudholt, Dirk}, year={2021}, pages={1–11} }"},"publication_identifier":{"isbn":["978-1-4503-8352-3"]},"publication_status":"published","title":"Do Additional Optima Speed up Evolutionary Algorithms?","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:45:31Z","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"last_name":"Sudholt","full_name":"Sudholt, Dirk","first_name":"Dirk"}],"date_created":"2023-11-14T15:58:55Z"},{"extern":"1","language":[{"iso":"eng"}],"keyword":["automated algorithm selection","graph theory","instance features","normalization","traveling salesperson problem (TSP)"],"user_id":"102979","department":[{"_id":"819"}],"_id":"48881","status":"public","abstract":[{"text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.","lang":"eng"}],"type":"book_chapter","publication":"Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","date_created":"2023-11-14T15:58:58Z","author":[{"last_name":"Heins","full_name":"Heins, Jonathan","first_name":"Jonathan"},{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"full_name":"Pohl, Janina","last_name":"Pohl","first_name":"Janina"},{"full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"}],"publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:47:23Z","citation":{"chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–15. New York, NY, USA: Association for Computing Machinery, 2021.","ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–15.","apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–15). Association for Computing Machinery.","mla":"Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–15.","bibtex":"@inbook{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={New York, NY, USA}, title={On the Potential of Normalized TSP Features for Automated Algorithm Selection}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2021}, pages={1–15} }","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15."},"page":"1–15","place":"New York, NY, USA","year":"2021","publication_identifier":{"isbn":["978-1-4503-8352-3"]}},{"author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Markus","last_name":"Wagner","full_name":"Wagner, Markus"}],"date_updated":"2023-12-13T10:47:41Z","doi":"10.1145/3449726.3463165","publication_identifier":{"isbn":["978-1-4503-8351-6"]},"citation":{"apa":"Bossek, J., &#38; Wagner, M. (2021). Generating Instances with Performance Differences for More than Just Two Algorithms. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1423–1432. <a href=\"https://doi.org/10.1145/3449726.3463165\">https://doi.org/10.1145/3449726.3463165</a>","short":"J. Bossek, M. Wagner, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1423–1432.","bibtex":"@inproceedings{Bossek_Wagner_2021, place={New York, NY, USA}, series={GECCO’21}, title={Generating Instances with Performance Differences for More than Just Two Algorithms}, DOI={<a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Wagner, Markus}, year={2021}, pages={1423–1432}, collection={GECCO’21} }","mla":"Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences for More than Just Two Algorithms.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2021, pp. 1423–1432, doi:<a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>.","ieee":"J. Bossek and M. Wagner, “Generating Instances with Performance Differences for More than Just Two Algorithms,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2021, pp. 1423–1432, doi: <a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>.","chicago":"Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences for More than Just Two Algorithms.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1423–1432. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449726.3463165\">https://doi.org/10.1145/3449726.3463165</a>.","ama":"Bossek J, Wagner M. Generating Instances with Performance Differences for More than Just Two Algorithms. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’21. Association for Computing Machinery; 2021:1423–1432. doi:<a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>"},"page":"1423–1432","place":"New York, NY, USA","user_id":"102979","series_title":"GECCO’21","department":[{"_id":"819"}],"_id":"48876","extern":"1","type":"conference","status":"public","date_created":"2023-11-14T15:58:57Z","publisher":"Association for Computing Machinery","title":"Generating Instances with Performance Differences for More than Just Two Algorithms","year":"2021","language":[{"iso":"eng"}],"keyword":["evolutionary algorithms","evolving instances","fitness function","instance hardness","traveling thief problem (TTP)"],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","abstract":[{"text":"In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity.","lang":"eng"}]},{"publication_identifier":{"isbn":["978-1-4503-8350-9"]},"page":"600–608","citation":{"ama":"Nikfarjam A, Bossek J, Neumann A, Neumann F. Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’21. Association for Computing Machinery; 2021:600–608. doi:<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>","ieee":"A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 600–608, doi: <a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>.","chicago":"Nikfarjam, Adel, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 600–608. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459384\">https://doi.org/10.1145/3449639.3459384</a>.","apa":"Nikfarjam, A., Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 600–608. <a href=\"https://doi.org/10.1145/3449639.3459384\">https://doi.org/10.1145/3449639.3459384</a>","bibtex":"@inproceedings{Nikfarjam_Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, series={GECCO’21}, title={Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={600–608}, collection={GECCO’21} }","short":"A. Nikfarjam, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 600–608.","mla":"Nikfarjam, Adel, et al. “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 600–608, doi:<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>."},"place":"New York, NY, USA","year":"2021","author":[{"full_name":"Nikfarjam, Adel","last_name":"Nikfarjam","first_name":"Adel"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"}],"date_created":"2023-11-14T15:59:00Z","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:50:06Z","doi":"10.1145/3449639.3459384","title":"Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","status":"public","abstract":[{"lang":"eng","text":"Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments."}],"department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO’21","_id":"48893","language":[{"iso":"eng"}],"extern":"1","keyword":["evolutionary algorithms","evolutionary diversity optimisation","high-order entropy","traveling salesperson problem"]},{"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems with uniform and knapsack constraints. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation (EDO) approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark problems and analyse trade-offs in terms of solution quality and diversity of the resulting solution sets.","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["evolutionary algorithms","evolutionary diversity optimisation","sub-modular functions"],"year":"2021","date_created":"2023-11-14T15:58:59Z","publisher":"Association for Computing Machinery","title":"Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions","type":"conference","status":"public","department":[{"_id":"819"}],"series_title":"GECCO’21","user_id":"102979","_id":"48891","extern":"1","publication_identifier":{"isbn":["978-1-4503-8350-9"]},"page":"261–269","citation":{"ama":"Neumann A, Bossek J, Neumann F. Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’21. Association for Computing Machinery; 2021:261–269. doi:<a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>","chicago":"Neumann, Aneta, Jakob Bossek, and Frank Neumann. “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 261–269. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459385\">https://doi.org/10.1145/3449639.3459385</a>.","ieee":"A. Neumann, J. Bossek, and F. Neumann, “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 261–269, doi: <a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>.","short":"A. Neumann, J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 261–269.","mla":"Neumann, Aneta, et al. “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 261–269, doi:<a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>.","bibtex":"@inproceedings{Neumann_Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO’21}, title={Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Neumann, Aneta and Bossek, Jakob and Neumann, Frank}, year={2021}, pages={261–269}, collection={GECCO’21} }","apa":"Neumann, A., Bossek, J., &#38; Neumann, F. (2021). Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 261–269. <a href=\"https://doi.org/10.1145/3449639.3459385\">https://doi.org/10.1145/3449639.3459385</a>"},"place":"New York, NY, USA","author":[{"full_name":"Neumann, Aneta","last_name":"Neumann","first_name":"Aneta"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"date_updated":"2023-12-13T10:49:25Z","doi":"10.1145/3449639.3459385"},{"status":"public","abstract":[{"text":"Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Often, it is desirable to compute not just a single solution for a given problem, but a diverse set of high quality solutions from which a decision maker can choose one for implementation. Currently, there are only a few approaches for computing a diverse solution set for the TSP. Furthermore, almost all of them assume that the optimal solution is known. In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown. We show how to adopt EAX to not only find a high-quality solution but also to maximise the diversity of the population. The resulting EAX-based EDO approach, termed EAX-EDO is capable of obtaining diverse high-quality tours when the optimal solution for the TSP is known or unknown. A comparison to existing approaches shows that they are clearly outperformed by EAX-EDO.","lang":"eng"}],"type":"book_chapter","publication":"Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms","language":[{"iso":"eng"}],"extern":"1","keyword":["edge assembly crossover (EAX)","evolutionary algorithms","evolutionary diversity optimisation (EDO)","traveling salesperson problem (TSP)"],"user_id":"102979","department":[{"_id":"819"}],"_id":"48892","citation":{"ama":"Nikfarjam A, Bossek J, Neumann A, Neumann F. Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation. In: <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–11.","chicago":"Nikfarjam, Adel, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.” In <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–11. New York, NY, USA: Association for Computing Machinery, 2021.","ieee":"A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation,” in <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.","apa":"Nikfarjam, A., Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation. In <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–11). Association for Computing Machinery.","mla":"Nikfarjam, Adel, et al. “Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.” <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–11.","bibtex":"@inbook{Nikfarjam_Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, title={Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation}, booktitle={Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={1–11} }","short":"A. Nikfarjam, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–11."},"page":"1–11","year":"2021","place":"New York, NY, USA","publication_identifier":{"isbn":["978-1-4503-8352-3"]},"title":"Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation","date_created":"2023-11-14T15:59:00Z","author":[{"last_name":"Nikfarjam","full_name":"Nikfarjam, Adel","first_name":"Adel"},{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"last_name":"Neumann","full_name":"Neumann, Aneta","first_name":"Aneta"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:49:59Z"},{"date_updated":"2023-12-13T10:51:34Z","volume":83,"date_created":"2023-11-14T15:58:54Z","author":[{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"},{"first_name":"Pan","full_name":"Peng, Pan","last_name":"Peng"},{"first_name":"Dirk","full_name":"Sudholt, Dirk","last_name":"Sudholt"}],"title":"Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem","doi":"10.1007/s00453-021-00838-3","publication_identifier":{"issn":["0178-4617"]},"issue":"10","year":"2021","intvolume":"        83","page":"3148–3179","citation":{"chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.” <i>Algorithmica</i> 83, no. 10 (2021): 3148–3179. <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">https://doi.org/10.1007/s00453-021-00838-3</a>.","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” <i>Algorithmica</i>, vol. 83, no. 10, pp. 3148–3179, 2021, doi: <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>.","ama":"Bossek J, Neumann F, Peng P, Sudholt D. Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. <i>Algorithmica</i>. 2021;83(10):3148–3179. doi:<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>","apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. <i>Algorithmica</i>, <i>83</i>(10), 3148–3179. <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">https://doi.org/10.1007/s00453-021-00838-3</a>","mla":"Bossek, Jakob, et al. “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.” <i>Algorithmica</i>, vol. 83, no. 10, 2021, pp. 3148–3179, doi:<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>.","bibtex":"@article{Bossek_Neumann_Peng_Sudholt_2021, title={Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}, volume={83}, DOI={<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>}, number={10}, journal={Algorithmica}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2021}, pages={3148–3179} }","short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, Algorithmica 83 (2021) 3148–3179."},"_id":"48854","department":[{"_id":"819"}],"user_id":"102979","keyword":["Dynamic optimization","Evolutionary algorithms","Running time analysis"],"language":[{"iso":"eng"}],"publication":"Algorithmica","type":"journal_article","abstract":[{"lang":"eng","text":"We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient."}],"status":"public"},{"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46315","language":[{"iso":"eng"}],"type":"conference","publication":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","status":"public","abstract":[{"lang":"eng","text":"We propose a novel method for automated algorithm selection in the domain of single-objective continuous black-box optimization. In contrast to existing methods, we use convolutional neural networks as the selection apparatus which bases its decision on a so-called ‘fitness map’. This fitness map is a 2D representation of a two dimensional search space where different gray scales indicate the quality of found solutions in certain areas. Our devised approach uses a modular CMA-ES framework which offers the option to create the conventional CMA-ES, CMA-ES with the alternate step-size adaptation and many other variants proposed over the years. In total, 4 608 different configurations are possible where most configurations are of complementary nature. In this proof-of-concept work, we consider a subset of 32 possible configurations. The developed method is evaluated against an excerpt of BBOB functions and its performance is compared against baselines that are commonly used in automated algorithm selection - the best standalone algorithm (configuration) and the best obtainable sequence of configurations. While the results indicate that the use of the fitness map is not superior on every benchmark problem, it indubitably shows its merit on more hard-to-solve problems. This offers a promising perspective for generalizing to other types of optimization problems and problem domains."}],"author":[{"last_name":"Prager","full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick"},{"id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"}],"date_created":"2023-08-04T07:25:08Z","date_updated":"2024-06-07T07:12:28Z","doi":"10.1109/SSCI50451.2021.9660174","title":"Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization","citation":{"ama":"Prager RP, Seiler M, Trautmann H, Kerschke P. Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In: <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2021:1-8. doi:<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>","ieee":"R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization,” in <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2021, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>.","chicago":"Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” In <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 1–8, 2021. <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">https://doi.org/10.1109/SSCI50451.2021.9660174</a>.","short":"R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8.","bibtex":"@inproceedings{Prager_Seiler_Trautmann_Kerschke_2021, title={Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization}, DOI={<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>}, booktitle={2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2021}, pages={1–8} }","mla":"Prager, Raphael Patrick, et al. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2021, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>.","apa":"Prager, R. P., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 1–8. <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">https://doi.org/10.1109/SSCI50451.2021.9660174</a>"},"page":"1-8","year":"2021"},{"author":[{"full_name":"Assenmacher, Dennis","last_name":"Assenmacher","first_name":"Dennis"},{"full_name":"Niemann, Marco","last_name":"Niemann","first_name":"Marco"},{"last_name":"Müller","full_name":"Müller, Kilian","first_name":"Kilian"},{"full_name":"Seiler, Moritz","id":"105520","last_name":"Seiler","first_name":"Moritz"},{"full_name":"Riehle, Dennis M.","last_name":"Riehle","first_name":"Dennis M."},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282"}],"date_created":"2023-08-04T07:22:59Z","date_updated":"2024-06-07T07:13:04Z","title":"RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets","page":"1–14","citation":{"ama":"Assenmacher D, Niemann M, Müller K, Seiler M, Riehle DM, Trautmann H. RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In: <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>. ; 2021:1–14.","chicago":"Assenmacher, Dennis, Marco Niemann, Kilian Müller, Moritz Seiler, Dennis M. Riehle, and Heike Trautmann. “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” In <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 1–14. Virtual Event, 2021.","ieee":"D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann, “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets,” in <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 2021, pp. 1–14.","short":"D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D.M. Riehle, H. Trautmann, in: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), Virtual Event, 2021, pp. 1–14.","mla":"Assenmacher, Dennis, et al. “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 2021, pp. 1–14.","bibtex":"@inproceedings{Assenmacher_Niemann_Müller_Seiler_Riehle_Trautmann_2021, place={Virtual Event}, title={RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)}, author={Assenmacher, Dennis and Niemann, Marco and Müller, Kilian and Seiler, Moritz and Riehle, Dennis M. and Trautmann, Heike}, year={2021}, pages={1–14} }","apa":"Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., &#38; Trautmann, H. (2021). RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 1–14."},"year":"2021","place":"Virtual Event","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46312","language":[{"iso":"eng"}],"publication":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)","type":"conference","status":"public","abstract":[{"lang":"eng","text":"Abuse and hate are penetrating social media and many comment sections of news media companies. These platform providers invest considerable efforts to mod- erate user-generated contributions to prevent losing readers who get appalled by inappropriate texts. This is further enforced by legislative actions, which make non-clearance of these comments a punishable action. While (semi-)automated solutions using Natural Language Processing and advanced Machine Learning techniques are getting increasingly sophisticated, the domain of abusive language detection still struggles as large non-English and well-curated datasets are scarce or not publicly available. With this work, we publish and analyse the largest annotated German abusive language comment datasets to date. In contrast to existing datasets, we achieve a high labelling standard by conducting a thorough crowd-based an- notation study that complements professional moderators’ decisions, which are also included in the dataset. We compare and cross-evaluate the performance of baseline algorithms and state-of-the-art transformer-based language models, which are fine-tuned on our datasets and an existing alternative, showing the usefulness for the community."}]},{"language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46313","status":"public","abstract":[{"text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.","lang":"eng"}],"editor":[{"last_name":"Computing Machinery Association","full_name":"Computing Machinery Association, for","first_name":"for"}],"type":"conference","publication":"Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)","doi":"10.1145/3450218.3477308","title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","author":[{"first_name":"Jonathan","last_name":"Heins","full_name":"Heins, Jonathan"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Janina","last_name":"Pohl","full_name":"Pohl, Janina"},{"last_name":"Seiler","full_name":"Seiler, Moritz","id":"105520","first_name":"Moritz"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"}],"date_created":"2023-08-04T07:23:57Z","publisher":"Association for Computing Machinery","date_updated":"2024-06-10T11:57:04Z","citation":{"apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In  for Computing Machinery Association (Ed.), <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)</i> (pp. 1–15). Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3450218.3477308\">https://doi.org/10.1145/3450218.3477308</a>","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in:  for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI), Association for Computing Machinery, Dornbirn, Austria, 2021, pp. 1–15.","mla":"Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>, edited by for Computing Machinery Association, Association for Computing Machinery, 2021, pp. 1–15, doi:<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>.","bibtex":"@inproceedings{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={Dornbirn, Austria}, title={On the Potential of Normalized TSP Features for Automated Algorithm Selection}, DOI={<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>}, booktitle={Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)}, publisher={Association for Computing Machinery}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, editor={Computing Machinery Association, for}, year={2021}, pages={1–15} }","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: Computing Machinery Association  for, ed. <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>. Association for Computing Machinery; 2021:1–15. doi:<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>","ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)</i>, 2021, pp. 1–15, doi: <a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>.","chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” In <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>, edited by for Computing Machinery Association, 1–15. Dornbirn, Austria: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3450218.3477308\">https://doi.org/10.1145/3450218.3477308</a>."},"page":"1–15","year":"2021","place":"Dornbirn, Austria"},{"language":[{"iso":"eng"}],"_id":"46319","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"abstract":[{"text":"The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than an- alyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and com- putational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learn- ing techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real- time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordi- nated campaigns.","lang":"eng"}],"status":"public","type":"conference","publication":"Proceedings of the Florida Artificial Intelligence Research Society Conference","title":"Towards Real-Time and Unsupervised Campaign Detection in Social Media","date_updated":"2023-10-16T12:59:10Z","date_created":"2023-08-04T07:29:36Z","author":[{"full_name":"Assenmacher, D","last_name":"Assenmacher","first_name":"D"},{"full_name":"Adam, L","last_name":"Adam","first_name":"L"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"},{"first_name":"C","full_name":"Grimme, C","last_name":"Grimme"}],"year":"2020","place":"Florida, USA","citation":{"ama":"Assenmacher D, Adam L, Trautmann H, Grimme C. Towards Real-Time and Unsupervised Campaign Detection in Social Media. In: <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>. ; 2020.","chicago":"Assenmacher, D, L Adam, Heike Trautmann, and C Grimme. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” In <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>. Florida, USA, 2020.","ieee":"D. Assenmacher, L. Adam, H. Trautmann, and C. Grimme, “Towards Real-Time and Unsupervised Campaign Detection in Social Media,” 2020.","apa":"Assenmacher, D., Adam, L., Trautmann, H., &#38; Grimme, C. (2020). Towards Real-Time and Unsupervised Campaign Detection in Social Media. <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>.","mla":"Assenmacher, D., et al. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>, 2020.","short":"D. Assenmacher, L. Adam, H. Trautmann, C. Grimme, in: Proceedings of the Florida Artificial Intelligence Research Society Conference, Florida, USA, 2020.","bibtex":"@inproceedings{Assenmacher_Adam_Trautmann_Grimme_2020, place={Florida, USA}, title={Towards Real-Time and Unsupervised Campaign Detection in Social Media}, booktitle={Proceedings of the Florida Artificial Intelligence Research Society Conference}, author={Assenmacher, D and Adam, L and Trautmann, Heike and Grimme, C}, year={2020} }"}},{"date_updated":"2023-10-16T13:04:15Z","date_created":"2023-08-04T07:37:30Z","author":[{"last_name":"Prager","full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"first_name":"Hao","last_name":"Wang","full_name":"Wang, Hao"},{"first_name":"Thomas H. W.","last_name":"Bäck","full_name":"Bäck, Thomas H. W."},{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"}],"title":"Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis","doi":"10.1109/SSCI47803.2020.9308510","place":"Canberra, Australia","year":"2020","page":"996–1003","citation":{"ieee":"R. P. Prager, H. Trautmann, H. Wang, T. H. W. Bäck, and P. Kerschke, “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis,” in <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 996–1003, doi: <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>.","chicago":"Prager, Raphael Patrick, Heike Trautmann, Hao Wang, Thomas H. W. Bäck, and Pascal Kerschke. “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” In <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 996–1003. Canberra, Australia, 2020. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">https://doi.org/10.1109/SSCI47803.2020.9308510</a>.","ama":"Prager RP, Trautmann H, Wang H, Bäck THW, Kerschke P. Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. In: <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2020:996–1003. doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>","mla":"Prager, Raphael Patrick, et al. “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 996–1003, doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>.","short":"R.P. Prager, H. Trautmann, H. Wang, T.H.W. Bäck, P. Kerschke, in: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 996–1003.","bibtex":"@inproceedings{Prager_Trautmann_Wang_Bäck_Kerschke_2020, place={Canberra, Australia}, title={Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis}, DOI={<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>}, booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Prager, Raphael Patrick and Trautmann, Heike and Wang, Hao and Bäck, Thomas H. W. and Kerschke, Pascal}, year={2020}, pages={996–1003} }","apa":"Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., &#38; Kerschke, P. (2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 996–1003. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">https://doi.org/10.1109/SSCI47803.2020.9308510</a>"},"_id":"46328","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","language":[{"iso":"eng"}],"publication":"Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)","type":"conference","abstract":[{"text":"In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.","lang":"eng"}],"status":"public"},{"page":"201–214","citation":{"ama":"Assenmacher D, Clever L, Pohl J, Trautmann H, Grimme C. A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In: Meiselwitz G, ed. <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>. Springer International Publishing; 2020:201–214. doi:<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>","chicago":"Assenmacher, D, L Clever, JS Pohl, Heike Trautmann, and C Grimme. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” In <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, edited by G Meiselwitz, 201–214. Cham: Springer International Publishing, 2020. <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">https://doi.org/10.1007/978-3-030-49570-1_14</a>.","ieee":"D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, and C. Grimme, “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media,” in <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, 2020, pp. 201–214, doi: <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>.","bibtex":"@inproceedings{Assenmacher_Clever_Pohl_Trautmann_Grimme_2020, place={Cham}, title={A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>}, booktitle={Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis}, publisher={Springer International Publishing}, author={Assenmacher, D and Clever, L and Pohl, JS and Trautmann, Heike and Grimme, C}, editor={Meiselwitz, G}, year={2020}, pages={201–214} }","short":"D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, C. Grimme, in: G. Meiselwitz (Ed.), Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis, Springer International Publishing, Cham, 2020, pp. 201–214.","mla":"Assenmacher, D., et al. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, edited by G Meiselwitz, Springer International Publishing, 2020, pp. 201–214, doi:<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>.","apa":"Assenmacher, D., Clever, L., Pohl, J., Trautmann, H., &#38; Grimme, C. (2020). A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In G. Meiselwitz (Ed.), <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i> (pp. 201–214). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">https://doi.org/10.1007/978-3-030-49570-1_14</a>"},"year":"2020","place":"Cham","publication_identifier":{"isbn":["978-3-030-49570-1"]},"doi":"10.1007/978-3-030-49570-1_14","title":"A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media","date_created":"2023-08-04T07:30:29Z","author":[{"first_name":"D","full_name":"Assenmacher, D","last_name":"Assenmacher"},{"first_name":"L","full_name":"Clever, L","last_name":"Clever"},{"first_name":"JS","full_name":"Pohl, JS","last_name":"Pohl"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike","first_name":"Heike"},{"first_name":"C","last_name":"Grimme","full_name":"Grimme, C"}],"publisher":"Springer International Publishing","date_updated":"2023-10-16T12:59:28Z","status":"public","editor":[{"full_name":"Meiselwitz, G","last_name":"Meiselwitz","first_name":"G"}],"abstract":[{"lang":"eng","text":"The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020."}],"publication":"Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46320"},{"language":[{"iso":"eng"}],"_id":"46325","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"abstract":[{"lang":"eng","text":"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."}],"status":"public","type":"conference","publication":"Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)","title":"Towards Automated Configuration of Stream Clustering Algorithms","doi":"10.1007/978-3-030-43823-4_12","date_updated":"2023-10-16T13:03:15Z","date_created":"2023-08-04T07:35:24Z","author":[{"full_name":"Carnein, Matthias","last_name":"Carnein","first_name":"Matthias"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"last_name":"Bifet","full_name":"Bifet, Albert","first_name":"Albert"},{"full_name":"Pfahringer, Bernhard","last_name":"Pfahringer","first_name":"Bernhard"}],"year":"2020","place":"Würzburg, Germany","citation":{"ama":"Carnein M, Trautmann H, Bifet A, Pfahringer B. Towards Automated Configuration of Stream Clustering Algorithms. In: <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>. ; 2020:137–143. doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>","ieee":"M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated Configuration of Stream Clustering Algorithms,” in <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi: <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>.","chicago":"Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “Towards Automated Configuration of Stream Clustering Algorithms.” In <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. Würzburg, Germany, 2020. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>.","mla":"Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering Algorithms.” <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>.","bibtex":"@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg, Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>}, booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020}, pages={137–143} }","short":"M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), Würzburg, Germany, 2020, pp. 137–143.","apa":"Carnein, M., Trautmann, H., Bifet, A., &#38; Pfahringer, B. (2020). Towards Automated Configuration of Stream Clustering Algorithms. <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>"},"page":"137–143","publication_identifier":{"isbn":["978-3-030-43823-4"]}},{"user_id":"15504","series_title":"Lecture Notes in Computer Science","department":[{"_id":"34"},{"_id":"819"}],"_id":"46321","language":[{"iso":"eng"}],"type":"conference","publication":"Disinformation in open online media","status":"public","editor":[{"first_name":"Christian","full_name":"Grimme, Christian","last_name":"Grimme"},{"full_name":"Preuß, Mike","last_name":"Preuß","first_name":"Mike"},{"last_name":"Takes","full_name":"Takes, Frank","first_name":"Frank"},{"full_name":"Waldherr, Annie","last_name":"Waldherr","first_name":"Annie"}],"abstract":[{"text":"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.","lang":"eng"}],"author":[{"full_name":"Assenmacher, Dennis","last_name":"Assenmacher","first_name":"Dennis"},{"first_name":"Lena","full_name":"Frischlich , Lena","last_name":"Frischlich "},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"last_name":"Adam","full_name":"Adam, Lena","first_name":"Lena"}],"date_created":"2023-08-04T07:31:13Z","date_updated":"2023-10-16T13:00:15Z","publisher":"Springer","title":"Inside the tool set of automation: Free social bot code revisited","citation":{"chicago":"Assenmacher, Dennis, Lena Frischlich , Heike Trautmann, Christian Grimme, and Lena Adam. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” In <i>Disinformation in Open Online Media</i>, edited by Christian Grimme, Mike Preuß, Frank Takes, and Annie Waldherr, 101–114. Lecture Notes in Computer Science. Wiesbaden: Springer, 2020.","ieee":"D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, and L. Adam, “Inside the tool set of automation: Free social bot code revisited,” in <i>Disinformation in open online media</i>, 2020, pp. 101–114.","ama":"Assenmacher D, Frischlich  L, Trautmann H, Grimme C, Adam L. Inside the tool set of automation: Free social bot code revisited. In: Grimme C, Preuß M, Takes F, Waldherr A, eds. <i>Disinformation in Open Online Media</i>. Lecture Notes in Computer Science. Springer; 2020:101–114.","bibtex":"@inproceedings{Assenmacher_Frischlich _Trautmann_Grimme_Adam_2020, place={Wiesbaden}, series={Lecture Notes in Computer Science}, title={Inside the tool set of automation: Free social bot code revisited}, booktitle={Disinformation in open online media}, publisher={Springer}, author={Assenmacher, Dennis and Frischlich , Lena and Trautmann, Heike and Grimme, Christian and Adam, Lena}, editor={Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={101–114}, collection={Lecture Notes in Computer Science} }","mla":"Assenmacher, Dennis, et al. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” <i>Disinformation in Open Online Media</i>, edited by Christian Grimme et al., Springer, 2020, pp. 101–114.","short":"D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, L. Adam, in: C. Grimme, M. Preuß, F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Springer, Wiesbaden, 2020, pp. 101–114.","apa":"Assenmacher, D., Frischlich , L., Trautmann, H., Grimme, C., &#38; Adam, L. (2020). Inside the tool set of automation: Free social bot code revisited. In C. Grimme, M. Preuß, F. Takes, &#38; A. Waldherr (Eds.), <i>Disinformation in open online media</i> (pp. 101–114). Springer."},"page":"101–114","year":"2020","place":"Wiesbaden"},{"language":[{"iso":"eng"}],"_id":"46326","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"abstract":[{"text":"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.","lang":"eng"}],"status":"public","type":"conference","publication":"Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)","title":"confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms","doi":"10.1007/978-3-030-53552-0_10","date_updated":"2023-10-16T13:03:36Z","author":[{"first_name":"Matthias","last_name":"Carnein","full_name":"Carnein, Matthias"},{"full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"first_name":"Albert","full_name":"Bifet, Albert","last_name":"Bifet"},{"first_name":"Bernhard","full_name":"Pfahringer, Bernhard","last_name":"Pfahringer"}],"date_created":"2023-08-04T07:36:03Z","year":"2020","place":"Athens, Greece","citation":{"chicago":"Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” In <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 80–95. Athens, Greece, 2020. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">https://doi.org/10.1007/978-3-030-53552-0_10</a>.","ieee":"M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms,” in <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 2020, pp. 80–95, doi: <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>.","ama":"Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. In: <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>. ; 2020:80–95. doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>","apa":"Carnein, M., Trautmann, H., Bifet, A., &#38; Pfahringer, B. (2020). confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 80–95. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">https://doi.org/10.1007/978-3-030-53552-0_10</a>","mla":"Carnein, Matthias, et al. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 2020, pp. 80–95, doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>.","bibtex":"@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Athens, Greece}, title={confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>}, booktitle={Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020}, pages={80–95} }","short":"M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020), Athens, Greece, 2020, pp. 80–95."},"page":"80–95"}]