---
_id: '48862'
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.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Sudholt D. Do Additional Optima Speed up Evolutionary Algorithms?
In: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms. Association for Computing Machinery; 2021:1–11.'
apa: Bossek, J., & Sudholt, D. (2021). Do Additional Optima Speed up Evolutionary
Algorithms? In Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms (pp. 1–11). Association for Computing Machinery.
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} }'
chicago: 'Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary
Algorithms?” In Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, 1–11. New York, NY, USA: Association for Computing
Machinery, 2021.'
ieee: 'J. Bossek and D. Sudholt, “Do Additional Optima Speed up Evolutionary Algorithms?,”
in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms, New York, NY, USA: Association for Computing Machinery, 2021,
pp. 1–11.'
mla: Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary
Algorithms?” Proceedings of the 16th ACM/SIGEVO Conference on Foundations of
Genetic Algorithms, Association for Computing Machinery, 2021, pp. 1–11.
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.'
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:45:31Z
department:
- _id: '819'
extern: '1'
keyword:
- evolutionary algorithms
- pseudo-boolean functions
- runtime analysis
- theory
language:
- iso: eng
page: 1–11
place: New York, NY, USA
publication: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- 978-1-4503-8352-3
publication_status: published
publisher: Association for Computing Machinery
status: public
title: Do Additional Optima Speed up Evolutionary Algorithms?
type: book_chapter
user_id: '102979'
year: '2021'
...
---
_id: '48881'
abstract:
- lang: eng
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.'
author:
- first_name: Jonathan
full_name: Heins, Jonathan
last_name: Heins
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Moritz
full_name: Seiler, Moritz
last_name: Seiler
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
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: Proceedings
of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association
for Computing Machinery; 2021:1–15.'
apa: Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke,
P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm
Selection. In Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms (pp. 1–15). Association for Computing Machinery.
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} }'
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 Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, 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
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms,
New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.'
mla: Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated
Algorithm Selection.” Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, Association for Computing Machinery, 2021, pp. 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.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:23Z
department:
- _id: '819'
extern: '1'
keyword:
- automated algorithm selection
- graph theory
- instance features
- normalization
- traveling salesperson problem (TSP)
language:
- iso: eng
page: 1–15
place: New York, NY, USA
publication: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- 978-1-4503-8352-3
publisher: Association for Computing Machinery
status: public
title: On the Potential of Normalized TSP Features for Automated Algorithm Selection
type: book_chapter
user_id: '102979'
year: '2021'
...
---
_id: '48876'
abstract:
- lang: eng
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.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
citation:
ama: 'Bossek J, Wagner M. Generating Instances with Performance Differences for
More than Just Two Algorithms. In: Proceedings of the Genetic and Evolutionary
Computation Conference Companion. GECCO’21. Association for Computing Machinery;
2021:1423–1432. doi:10.1145/3449726.3463165'
apa: Bossek, J., & Wagner, M. (2021). Generating Instances with Performance
Differences for More than Just Two Algorithms. Proceedings of the Genetic and
Evolutionary Computation Conference Companion, 1423–1432. https://doi.org/10.1145/3449726.3463165
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={10.1145/3449726.3463165},
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} }'
chicago: 'Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance
Differences for More than Just Two Algorithms.” In Proceedings of the Genetic
and Evolutionary Computation Conference Companion, 1423–1432. GECCO’21. New
York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3449726.3463165.'
ieee: 'J. Bossek and M. Wagner, “Generating Instances with Performance Differences
for More than Just Two Algorithms,” in Proceedings of the Genetic and Evolutionary
Computation Conference Companion, 2021, pp. 1423–1432, doi: 10.1145/3449726.3463165.'
mla: Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences
for More than Just Two Algorithms.” Proceedings of the Genetic and Evolutionary
Computation Conference Companion, Association for Computing Machinery, 2021,
pp. 1423–1432, doi:10.1145/3449726.3463165.
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.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:41Z
department:
- _id: '819'
doi: 10.1145/3449726.3463165
extern: '1'
keyword:
- evolutionary algorithms
- evolving instances
- fitness function
- instance hardness
- traveling thief problem (TTP)
language:
- iso: eng
page: 1423–1432
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-8351-6
publisher: Association for Computing Machinery
series_title: GECCO’21
status: public
title: Generating Instances with Performance Differences for More than Just Two Algorithms
type: conference
user_id: '102979'
year: '2021'
...
---
_id: '48893'
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.
author:
- first_name: Adel
full_name: Nikfarjam, Adel
last_name: Nikfarjam
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Nikfarjam A, Bossek J, Neumann A, Neumann F. Entropy-Based Evolutionary Diversity
Optimisation for the Traveling Salesperson Problem. In: Proceedings of the
Genetic and Evolutionary Computation Conference. GECCO’21. Association for
Computing Machinery; 2021:600–608. doi:10.1145/3449639.3459384'
apa: Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Entropy-Based
Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. Proceedings
of the Genetic and Evolutionary Computation Conference, 600–608. https://doi.org/10.1145/3449639.3459384
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={10.1145/3449639.3459384},
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}
}'
chicago: 'Nikfarjam, Adel, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Entropy-Based
Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.” In
Proceedings of the Genetic and Evolutionary Computation Conference, 600–608.
GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3449639.3459384.'
ieee: 'A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Entropy-Based Evolutionary
Diversity Optimisation for the Traveling Salesperson Problem,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2021, pp. 600–608,
doi: 10.1145/3449639.3459384.'
mla: Nikfarjam, Adel, et al. “Entropy-Based Evolutionary Diversity Optimisation
for the Traveling Salesperson Problem.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2021, pp. 600–608,
doi:10.1145/3449639.3459384.
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.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:50:06Z
department:
- _id: '819'
doi: 10.1145/3449639.3459384
extern: '1'
keyword:
- evolutionary algorithms
- evolutionary diversity optimisation
- high-order entropy
- traveling salesperson problem
language:
- iso: eng
page: 600–608
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-8350-9
publisher: Association for Computing Machinery
series_title: GECCO’21
status: public
title: Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson
Problem
type: conference
user_id: '102979'
year: '2021'
...
---
_id: '48891'
abstract:
- lang: eng
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.
author:
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Neumann A, Bossek J, Neumann F. Diversifying Greedy Sampling and Evolutionary
Diversity Optimisation for Constrained Monotone Submodular Functions. In: Proceedings
of the Genetic and Evolutionary Computation Conference. GECCO’21. Association
for Computing Machinery; 2021:261–269. doi:10.1145/3449639.3459385'
apa: Neumann, A., Bossek, J., & Neumann, F. (2021). Diversifying Greedy Sampling
and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions.
Proceedings of the Genetic and Evolutionary Computation Conference, 261–269.
https://doi.org/10.1145/3449639.3459385
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={10.1145/3449639.3459385},
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}
}'
chicago: 'Neumann, Aneta, Jakob Bossek, and Frank Neumann. “Diversifying Greedy
Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular
Functions.” In Proceedings of the Genetic and Evolutionary Computation Conference,
261–269. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021.
https://doi.org/10.1145/3449639.3459385.'
ieee: 'A. Neumann, J. Bossek, and F. Neumann, “Diversifying Greedy Sampling and
Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions,”
in Proceedings of the Genetic and Evolutionary Computation Conference,
2021, pp. 261–269, doi: 10.1145/3449639.3459385.'
mla: Neumann, Aneta, et al. “Diversifying Greedy Sampling and Evolutionary Diversity
Optimisation for Constrained Monotone Submodular Functions.” Proceedings of
the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2021, pp. 261–269, doi:10.1145/3449639.3459385.
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.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:49:25Z
department:
- _id: '819'
doi: 10.1145/3449639.3459385
extern: '1'
keyword:
- evolutionary algorithms
- evolutionary diversity optimisation
- sub-modular functions
language:
- iso: eng
page: 261–269
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-8350-9
publisher: Association for Computing Machinery
series_title: GECCO’21
status: public
title: Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained
Monotone Submodular Functions
type: conference
user_id: '102979'
year: '2021'
...
---
_id: '48892'
abstract:
- lang: eng
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.
author:
- first_name: Adel
full_name: Nikfarjam, Adel
last_name: Nikfarjam
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
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: Proceedings
of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms. Association
for Computing Machinery; 2021:1–11.'
apa: Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Computing
Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation.
In Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic
Algorithms (pp. 1–11). Association for Computing Machinery.
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} }'
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 Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic
Algorithms, 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
Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms,
New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.'
mla: Nikfarjam, Adel, et al. “Computing Diverse Sets of High Quality TSP Tours by
EAX-Based Evolutionary Diversity Optimisation.” Proceedings of the 16th ACM}/SIGEVO
Conference on Foundations of Genetic Algorithms, Association for Computing
Machinery, 2021, pp. 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.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:59Z
department:
- _id: '819'
extern: '1'
keyword:
- edge assembly crossover (EAX)
- evolutionary algorithms
- evolutionary diversity optimisation (EDO)
- traveling salesperson problem (TSP)
language:
- iso: eng
page: 1–11
place: New York, NY, USA
publication: Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- 978-1-4503-8352-3
publisher: Association for Computing Machinery
status: public
title: Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary
Diversity Optimisation
type: book_chapter
user_id: '102979'
year: '2021'
...
---
_id: '48854'
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.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: Bossek J, Neumann F, Peng P, Sudholt D. Time Complexity Analysis of Randomized
Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica.
2021;83(10):3148–3179. doi:10.1007/s00453-021-00838-3
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2021). Time Complexity
Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.
Algorithmica, 83(10), 3148–3179. https://doi.org/10.1007/s00453-021-00838-3
bibtex: '@article{Bossek_Neumann_Peng_Sudholt_2021, title={Time Complexity Analysis
of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}, volume={83},
DOI={10.1007/s00453-021-00838-3},
number={10}, journal={Algorithmica}, author={Bossek, Jakob and Neumann, Frank
and Peng, Pan and Sudholt, Dirk}, year={2021}, pages={3148–3179} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Time Complexity
Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.”
Algorithmica 83, no. 10 (2021): 3148–3179. https://doi.org/10.1007/s00453-021-00838-3.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis
of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” Algorithmica,
vol. 83, no. 10, pp. 3148–3179, 2021, doi: 10.1007/s00453-021-00838-3.'
mla: Bossek, Jakob, et al. “Time Complexity Analysis of Randomized Search Heuristics
for the Dynamic Graph Coloring Problem.” Algorithmica, vol. 83, no. 10,
2021, pp. 3148–3179, doi:10.1007/s00453-021-00838-3.
short: J. Bossek, F. Neumann, P. Peng, D. Sudholt, Algorithmica 83 (2021) 3148–3179.
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:51:34Z
department:
- _id: '819'
doi: 10.1007/s00453-021-00838-3
intvolume: ' 83'
issue: '10'
keyword:
- Dynamic optimization
- Evolutionary algorithms
- Running time analysis
language:
- iso: eng
page: 3148–3179
publication: Algorithmica
publication_identifier:
issn:
- 0178-4617
status: public
title: Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph
Coloring Problem
type: journal_article
user_id: '102979'
volume: 83
year: '2021'
...
---
_id: '46315'
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:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Prager RP, Seiler M, Trautmann H, Kerschke P. Towards Feature-Free Automated
Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In:
2021 IEEE Symposium Series on Computational Intelligence (SSCI). ; 2021:1-8.
doi:10.1109/SSCI50451.2021.9660174'
apa: Prager, R. P., Seiler, M., Trautmann, H., & Kerschke, P. (2021). Towards
Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box
Optimization. 2021 IEEE Symposium Series on Computational Intelligence (SSCI),
1–8. https://doi.org/10.1109/SSCI50451.2021.9660174
bibtex: '@inproceedings{Prager_Seiler_Trautmann_Kerschke_2021, title={Towards Feature-Free
Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization},
DOI={10.1109/SSCI50451.2021.9660174},
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} }'
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 2021 IEEE Symposium Series on Computational Intelligence
(SSCI), 1–8, 2021. https://doi.org/10.1109/SSCI50451.2021.9660174.
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 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021,
pp. 1–8, doi: 10.1109/SSCI50451.2021.9660174.'
mla: Prager, Raphael Patrick, et al. “Towards Feature-Free Automated Algorithm Selection
for Single-Objective Continuous Black-Box Optimization.” 2021 IEEE Symposium
Series on Computational Intelligence (SSCI), 2021, pp. 1–8, doi:10.1109/SSCI50451.2021.9660174.
short: 'R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: 2021 IEEE Symposium
Series on Computational Intelligence (SSCI), 2021, pp. 1–8.'
date_created: 2023-08-04T07:25:08Z
date_updated: 2024-06-07T07:12:28Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/SSCI50451.2021.9660174
language:
- iso: eng
page: 1-8
publication: 2021 IEEE Symposium Series on Computational Intelligence (SSCI)
status: public
title: Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous
Black-Box Optimization
type: conference
user_id: '15504'
year: '2021'
...
---
_id: '46312'
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.
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Marco
full_name: Niemann, Marco
last_name: Niemann
- first_name: Kilian
full_name: Müller, Kilian
last_name: Müller
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Dennis M.
full_name: Riehle, Dennis M.
last_name: Riehle
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Assenmacher D, Niemann M, Müller K, Seiler M, Riehle DM, Trautmann H. RP-Mod
& RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In:
Proceedings of the Neural Information Processing Systems Track on Datasets
and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021). ; 2021:1–14.'
apa: 'Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., &
Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German
News Comment Datasets. Proceedings of the Neural Information Processing Systems
Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021),
1–14.'
bibtex: '@inproceedings{Assenmacher_Niemann_Müller_Seiler_Riehle_Trautmann_2021,
place={Virtual Event}, title={RP-Mod & 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}
}'
chicago: 'Assenmacher, Dennis, Marco Niemann, Kilian Müller, Moritz Seiler, Dennis
M. Riehle, and Heike Trautmann. “RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated
German News Comment Datasets.” In Proceedings of the Neural Information Processing
Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021),
1–14. Virtual Event, 2021.'
ieee: 'D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann,
“RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets,”
in Proceedings of the Neural Information Processing Systems Track on Datasets
and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 2021, pp. 1–14.'
mla: 'Assenmacher, Dennis, et al. “RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated
German News Comment Datasets.” Proceedings of the Neural Information Processing
Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021),
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.'
date_created: 2023-08-04T07:22:59Z
date_updated: 2024-06-07T07:13:04Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 1–14
place: Virtual Event
publication: Proceedings of the Neural Information Processing Systems Track on Datasets
and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)
status: public
title: 'RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets'
type: conference
user_id: '15504'
year: '2021'
...
---
_id: '46313'
abstract:
- lang: eng
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.'
author:
- first_name: Jonathan
full_name: Heins, Jonathan
last_name: Heins
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
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. Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations
of Genetic Algorithms (FOGA XVI). Association for Computing Machinery; 2021:1–15.
doi:10.1145/3450218.3477308'
apa: Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke,
P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm
Selection. In for Computing Machinery Association (Ed.), Proceedings of the
16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)
(pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3450218.3477308
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={10.1145/3450218.3477308},
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} }'
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 Proceedings of the 16$^th$ ACM/SIGEVO Conference on
Foundations of Genetic Algorithms (FOGA XVI), edited by for Computing Machinery
Association, 1–15. Dornbirn, Austria: Association for Computing Machinery, 2021.
https://doi.org/10.1145/3450218.3477308.'
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
Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic
Algorithms (FOGA XVI), 2021, pp. 1–15, doi: 10.1145/3450218.3477308.'
mla: Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated
Algorithm Selection.” Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations
of Genetic Algorithms (FOGA XVI), edited by for Computing Machinery Association,
Association for Computing Machinery, 2021, pp. 1–15, doi:10.1145/3450218.3477308.
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.'
date_created: 2023-08-04T07:23:57Z
date_updated: 2024-06-10T11:57:04Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3450218.3477308
editor:
- first_name: for
full_name: Computing Machinery Association, for
last_name: Computing Machinery Association
language:
- iso: eng
page: 1–15
place: Dornbirn, Austria
publication: Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic
Algorithms (FOGA XVI)
publisher: Association for Computing Machinery
status: public
title: On the Potential of Normalized TSP Features for Automated Algorithm Selection
type: conference
user_id: '15504'
year: '2021'
...
---
_id: '46319'
abstract:
- lang: eng
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.
author:
- first_name: D
full_name: Assenmacher, D
last_name: Assenmacher
- first_name: L
full_name: Adam, L
last_name: Adam
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: C
full_name: Grimme, C
last_name: Grimme
citation:
ama: 'Assenmacher D, Adam L, Trautmann H, Grimme C. Towards Real-Time and Unsupervised
Campaign Detection in Social Media. In: Proceedings of the Florida Artificial
Intelligence Research Society Conference. ; 2020.'
apa: Assenmacher, D., Adam, L., Trautmann, H., & Grimme, C. (2020). Towards
Real-Time and Unsupervised Campaign Detection in Social Media. Proceedings
of the Florida Artificial Intelligence Research Society Conference.
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} }'
chicago: Assenmacher, D, L Adam, Heike Trautmann, and C Grimme. “Towards Real-Time
and Unsupervised Campaign Detection in Social Media.” In Proceedings of the
Florida Artificial Intelligence Research Society Conference. Florida, USA,
2020.
ieee: D. Assenmacher, L. Adam, H. Trautmann, and C. Grimme, “Towards Real-Time and
Unsupervised Campaign Detection in Social Media,” 2020.
mla: Assenmacher, D., et al. “Towards Real-Time and Unsupervised Campaign Detection
in Social Media.” Proceedings of the Florida Artificial Intelligence Research
Society Conference, 2020.
short: 'D. Assenmacher, L. Adam, H. Trautmann, C. Grimme, in: Proceedings of the
Florida Artificial Intelligence Research Society Conference, Florida, USA, 2020.'
date_created: 2023-08-04T07:29:36Z
date_updated: 2023-10-16T12:59:10Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
place: Florida, USA
publication: Proceedings of the Florida Artificial Intelligence Research Society Conference
status: public
title: Towards Real-Time and Unsupervised Campaign Detection in Social Media
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46328'
abstract:
- lang: eng
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.
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Thomas H. W.
full_name: Bäck, Thomas H. W.
last_name: Bäck
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
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: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI). ; 2020:996–1003. doi:10.1109/SSCI47803.2020.9308510'
apa: Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., & Kerschke, P.
(2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier
Chains and Exploratory Landscape Analysis. Proceedings of the IEEE Symposium
Series on Computational Intelligence (SSCI), 996–1003. https://doi.org/10.1109/SSCI47803.2020.9308510
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={10.1109/SSCI47803.2020.9308510},
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} }'
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 Proceedings
of the IEEE Symposium Series on Computational Intelligence (SSCI), 996–1003.
Canberra, Australia, 2020. https://doi.org/10.1109/SSCI47803.2020.9308510.
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 Proceedings of the IEEE Symposium Series on Computational
Intelligence (SSCI), 2020, pp. 996–1003, doi: 10.1109/SSCI47803.2020.9308510.'
mla: Prager, Raphael Patrick, et al. “Per-Instance Configuration of the Modularized
CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” Proceedings
of the IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp.
996–1003, doi:10.1109/SSCI47803.2020.9308510.
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.'
date_created: 2023-08-04T07:37:30Z
date_updated: 2023-10-16T13:04:15Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/SSCI47803.2020.9308510
language:
- iso: eng
page: 996–1003
place: Canberra, Australia
publication: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI)
status: public
title: Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier
Chains and Exploratory Landscape Analysis
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46320'
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.
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
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: C
full_name: Grimme, C
last_name: Grimme
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. 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; 2020:201–214. doi:10.1007/978-3-030-49570-1_14'
apa: 'Assenmacher, D., Clever, L., Pohl, J., Trautmann, H., & Grimme, C. (2020).
A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. 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 (pp. 201–214). Springer International
Publishing. https://doi.org/10.1007/978-3-030-49570-1_14'
bibtex: '@inproceedings{Assenmacher_Clever_Pohl_Trautmann_Grimme_2020, place={Cham},
title={A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media},
DOI={10.1007/978-3-030-49570-1_14},
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} }'
chicago: 'Assenmacher, D, L Clever, JS Pohl, Heike Trautmann, and C Grimme. “A Two-Phase
Framework for Detecting Manipulation Campaigns in Social Media.” In Proceedings
of the International Conference on Human-Computer Interaction (HCII 2020): Social
Computing and Social Media. Design, Ethics, User Behavior, and Social Network
Analysis, edited by G Meiselwitz, 201–214. Cham: Springer International Publishing,
2020. https://doi.org/10.1007/978-3-030-49570-1_14.'
ieee: 'D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, and C. Grimme, “A Two-Phase
Framework for Detecting Manipulation Campaigns in Social Media,” in Proceedings
of the International Conference on Human-Computer Interaction (HCII 2020): Social
Computing and Social Media. Design, Ethics, User Behavior, and Social Network
Analysis, 2020, pp. 201–214, doi: 10.1007/978-3-030-49570-1_14.'
mla: 'Assenmacher, D., et al. “A Two-Phase Framework for Detecting Manipulation
Campaigns in Social Media.” Proceedings of the International Conference on
Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design,
Ethics, User Behavior, and Social Network Analysis, edited by G Meiselwitz,
Springer International Publishing, 2020, pp. 201–214, doi:10.1007/978-3-030-49570-1_14.'
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.'
date_created: 2023-08-04T07:30:29Z
date_updated: 2023-10-16T12:59:28Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-49570-1_14
editor:
- first_name: G
full_name: Meiselwitz, G
last_name: Meiselwitz
language:
- iso: eng
page: 201–214
place: Cham
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'
publication_identifier:
isbn:
- 978-3-030-49570-1
publisher: Springer International Publishing
status: public
title: A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46325'
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.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Albert
full_name: Bifet, Albert
last_name: Bifet
- first_name: Bernhard
full_name: Pfahringer, Bernhard
last_name: Pfahringer
citation:
ama: 'Carnein M, Trautmann H, Bifet A, Pfahringer B. Towards Automated Configuration
of Stream Clustering Algorithms. In: Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECMLPKDD ’19). ; 2020:137–143. doi:10.1007/978-3-030-43823-4_12'
apa: Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). Towards
Automated Configuration of Stream Clustering Algorithms. Proceedings of the
European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECMLPKDD ’19), 137–143. https://doi.org/10.1007/978-3-030-43823-4_12
bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg,
Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms},
DOI={10.1007/978-3-030-43823-4_12},
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} }'
chicago: Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer.
“Towards Automated Configuration of Stream Clustering Algorithms.” In Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Databases (ECMLPKDD ’19), 137–143. Würzburg, Germany,
2020. https://doi.org/10.1007/978-3-030-43823-4_12.
ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated
Configuration of Stream Clustering Algorithms,” in Proceedings of the European
Conference on Machine Learning and Principles and Practice of Knowledge Discovery
in Databases (ECMLPKDD ’19), 2020, pp. 137–143, doi: 10.1007/978-3-030-43823-4_12.'
mla: Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering
Algorithms.” Proceedings of the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19),
2020, pp. 137–143, doi:10.1007/978-3-030-43823-4_12.
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.'
date_created: 2023-08-04T07:35:24Z
date_updated: 2023-10-16T13:03:15Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-43823-4_12
language:
- iso: eng
page: 137–143
place: Würzburg, Germany
publication: Proceedings of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)
publication_identifier:
isbn:
- 978-3-030-43823-4
status: public
title: Towards Automated Configuration of Stream Clustering Algorithms
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46321'
abstract:
- lang: eng
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.
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Lena
full_name: Frischlich , Lena
last_name: 'Frischlich '
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Lena
full_name: Adam, Lena
last_name: Adam
citation:
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. Disinformation in Open Online Media. Lecture Notes
in Computer Science. Springer; 2020:101–114.'
apa: 'Assenmacher, D., Frischlich , L., Trautmann, H., Grimme, C., & Adam, L.
(2020). Inside the tool set of automation: Free social bot code revisited. In
C. Grimme, M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in
open online media (pp. 101–114). Springer.'
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} }'
chicago: 'Assenmacher, Dennis, Lena Frischlich , Heike Trautmann, Christian Grimme,
and Lena Adam. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.”
In Disinformation in Open Online Media, 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 Disinformation
in open online media, 2020, pp. 101–114.'
mla: 'Assenmacher, Dennis, et al. “Inside the Tool Set of Automation: Free Social
Bot Code Revisited.” Disinformation in Open Online Media, 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.'
date_created: 2023-08-04T07:31:13Z
date_updated: 2023-10-16T13:00:15Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
- first_name: Frank
full_name: Takes, Frank
last_name: Takes
- first_name: Annie
full_name: Waldherr, Annie
last_name: Waldherr
language:
- iso: eng
page: 101–114
place: Wiesbaden
publication: Disinformation in open online media
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Inside the tool set of automation: Free social bot code revisited'
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46326'
abstract:
- lang: eng
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.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Albert
full_name: Bifet, Albert
last_name: Bifet
- first_name: Bernhard
full_name: Pfahringer, Bernhard
last_name: Pfahringer
citation:
ama: 'Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm
Selection and Configuration of Stream Clustering Algorithms. In: Proceedings
of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020).
; 2020:80–95. doi:10.1007/978-3-030-53552-0_10'
apa: 'Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). confStream:
Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.
Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020), 80–95. https://doi.org/10.1007/978-3-030-53552-0_10'
bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Athens,
Greece}, title={confStream: Automated Algorithm Selection and Configuration of
Stream Clustering Algorithms}, DOI={10.1007/978-3-030-53552-0_10},
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} }'
chicago: 'Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer.
“ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering
Algorithms.” In Proceedings of the 14$^th$ Learning and Intelligent Optimization
Conference (LION 2020), 80–95. Athens, Greece, 2020. https://doi.org/10.1007/978-3-030-53552-0_10.'
ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “confStream: Automated
Algorithm Selection and Configuration of Stream Clustering Algorithms,” in Proceedings
of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020),
2020, pp. 80–95, doi: 10.1007/978-3-030-53552-0_10.'
mla: 'Carnein, Matthias, et al. “ConfStream: Automated Algorithm Selection and Configuration
of Stream Clustering Algorithms.” Proceedings of the 14$^th$ Learning and Intelligent
Optimization Conference (LION 2020), 2020, pp. 80–95, doi:10.1007/978-3-030-53552-0_10.'
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.'
date_created: 2023-08-04T07:36:03Z
date_updated: 2023-10-16T13:03:36Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-53552-0_10
language:
- iso: eng
page: 80–95
place: Athens, Greece
publication: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020)
status: public
title: 'confStream: Automated Algorithm Selection and Configuration of Stream Clustering
Algorithms'
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46327'
abstract:
- lang: eng
text: In online media environments, nostalgia can be used as important ingredient
of propaganda strategies, specifically, by creating societal pessimism. This work
addresses the automated detection of nostalgic text as a first step towards automatically
identifying nostalgia-based manipulation strategies. We compare the performance
of standard machine learning approaches on this challenge and demonstrate the
successful transfer of the best performing approach to real-world nostalgia detection
in a case study.
author:
- first_name: Clever
full_name: Lena, Clever
last_name: Lena
- first_name: Lena
full_name: Frischlich, Lena
last_name: Frischlich
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Lena C, Frischlich L, Trautmann H, Grimme C. Automated detection of nostalgic
text in the context of societal pessimism. In: Grimme C, Preuß M, Takes F, Waldherr
A, eds. Disinformation in Open Online Media. ; 2020:48–58.'
apa: Lena, C., Frischlich, L., Trautmann, H., & Grimme, C. (2020). Automated
detection of nostalgic text in the context of societal pessimism. In C. Grimme,
M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in open online
media (pp. 48–58).
bibtex: '@inproceedings{Lena_Frischlich_Trautmann_Grimme_2020, place={Hamburg, Deutschland},
title={Automated detection of nostalgic text in the context of societal pessimism},
booktitle={Disinformation in open online media}, author={Lena, Clever and Frischlich,
Lena and Trautmann, Heike and Grimme, Christian}, editor={Grimme, Christian and
Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={48–58}
}'
chicago: Lena, Clever, Lena Frischlich, Heike Trautmann, and Christian Grimme. “Automated
Detection of Nostalgic Text in the Context of Societal Pessimism.” In Disinformation
in Open Online Media, edited by Christian Grimme, Mike Preuß, Frank Takes,
and Annie Waldherr, 48–58. Hamburg, Deutschland, 2020.
ieee: C. Lena, L. Frischlich, H. Trautmann, and C. Grimme, “Automated detection
of nostalgic text in the context of societal pessimism,” in Disinformation
in open online media, 2020, pp. 48–58.
mla: Lena, Clever, et al. “Automated Detection of Nostalgic Text in the Context
of Societal Pessimism.” Disinformation in Open Online Media, edited by
Christian Grimme et al., 2020, pp. 48–58.
short: 'C. Lena, L. Frischlich, H. Trautmann, C. Grimme, in: C. Grimme, M. Preuß,
F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Hamburg, Deutschland,
2020, pp. 48–58.'
date_created: 2023-08-04T07:36:43Z
date_updated: 2023-10-16T13:03:56Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
- first_name: Frank
full_name: Takes, Frank
last_name: Takes
- first_name: Annie
full_name: Waldherr, Annie
last_name: Waldherr
language:
- iso: eng
page: 48–58
place: Hamburg, Deutschland
publication: Disinformation in open online media
status: public
title: Automated detection of nostalgic text in the context of societal pessimism
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46329'
abstract:
- lang: eng
text: The past decade has been characterized by a strong increase in the use of
social media and a continuous growth of public online discussion. With the failure
of purely manual moderation, platform operators started searching for semi-automated
solutions, where the application of Natural Language Processing (NLP) and Machine
Learning (ML) techniques is promising. However, this requires huge financial investments
for algorithmic implementations, data collection, and model training, which only
big players can afford. To support smaller or medium-sized media enterprises (SME),
we developed an integrated comment moderation system as an IT platform. This platform
acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating
such a platform, however, requires a robust technology stack, integrated workflows
and well-defined interfaces between all parties. In this paper, we develop and
discuss a suitable IT architecture and present a prototypical implementation.
author:
- first_name: Dennis M.
full_name: Riehle, Dennis M.
last_name: Riehle
- first_name: Marco
full_name: Niemann, Marco
last_name: Niemann
- first_name: Jens
full_name: Brunk, Jens
last_name: Brunk
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Jörg
full_name: Becker, Jörg
last_name: Becker
citation:
ama: 'Riehle DM, Niemann M, Brunk J, Assenmacher D, Trautmann H, Becker J. Building
an Integrated Comment Moderation System – Towards a Semi-automatic Moderation
Tool. In: Meiselwitz G, ed. Social Computing and Social Media. Participation,
User Experience, Consumer Experience, and Applications of Social Computing.
Springer International Publishing; 2020:71–86.'
apa: Riehle, D. M., Niemann, M., Brunk, J., Assenmacher, D., Trautmann, H., &
Becker, J. (2020). Building an Integrated Comment Moderation System – Towards
a Semi-automatic Moderation Tool. In G. Meiselwitz (Ed.), Social Computing
and Social Media. Participation, User Experience, Consumer Experience, and Applications
of Social Computing (pp. 71–86). Springer International Publishing.
bibtex: '@inproceedings{Riehle_Niemann_Brunk_Assenmacher_Trautmann_Becker_2020,
place={Cham}, title={Building an Integrated Comment Moderation System – Towards
a Semi-automatic Moderation Tool}, booktitle={Social Computing and Social Media.
Participation, User Experience, Consumer Experience, and Applications of Social
Computing}, publisher={Springer International Publishing}, author={Riehle, Dennis
M. and Niemann, Marco and Brunk, Jens and Assenmacher, Dennis and Trautmann, Heike
and Becker, Jörg}, editor={Meiselwitz, Gabriele}, year={2020}, pages={71–86} }'
chicago: 'Riehle, Dennis M., Marco Niemann, Jens Brunk, Dennis Assenmacher, Heike
Trautmann, and Jörg Becker. “Building an Integrated Comment Moderation System
– Towards a Semi-Automatic Moderation Tool.” In Social Computing and Social
Media. Participation, User Experience, Consumer Experience, and Applications of
Social Computing, edited by Gabriele Meiselwitz, 71–86. Cham: Springer International
Publishing, 2020.'
ieee: D. M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, and J. Becker,
“Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation
Tool,” in Social Computing and Social Media. Participation, User Experience,
Consumer Experience, and Applications of Social Computing, 2020, pp. 71–86.
mla: Riehle, Dennis M., et al. “Building an Integrated Comment Moderation System
– Towards a Semi-Automatic Moderation Tool.” Social Computing and Social Media.
Participation, User Experience, Consumer Experience, and Applications of Social
Computing, edited by Gabriele Meiselwitz, Springer International Publishing,
2020, pp. 71–86.
short: 'D.M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, J. Becker,
in: G. Meiselwitz (Ed.), Social Computing and Social Media. Participation, User
Experience, Consumer Experience, and Applications of Social Computing, Springer
International Publishing, Cham, 2020, pp. 71–86.'
date_created: 2023-08-04T07:38:42Z
date_updated: 2023-10-16T13:04:36Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Gabriele
full_name: Meiselwitz, Gabriele
last_name: Meiselwitz
language:
- iso: eng
page: 71–86
place: Cham
publication: Social Computing and Social Media. Participation, User Experience, Consumer
Experience, and Applications of Social Computing
publication_identifier:
isbn:
- 978-3-030-49576-3
publisher: Springer International Publishing
status: public
title: Building an Integrated Comment Moderation System – Towards a Semi-automatic
Moderation Tool
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46333'
abstract:
- lang: eng
text: ' Recently, social bots, (semi-) automatized accounts in social media, gained
global attention in the context of public opinion manipulation. Dystopian scenarios
like the malicious amplification of topics, the spreading of disinformation, and
the manipulation of elections through “opinion machines” created headlines around
the globe. As a consequence, much research effort has been put into the classification
and detection of social bots. Yet, it is still unclear how easy an average online
media user can purchase social bots, which platforms they target, where they originate
from, and how sophisticated these bots are. This work provides a much needed new
perspective on these questions. By providing insights into the markets of social
bots in the clearnet and darknet as well as an exhaustive analysis of freely available
software tools for automation during the last decade, we shed light on the availability
and capabilities of automated profiles in social media platforms. Our results
confirm the increasing importance of social bot technology but also uncover an
as yet unknown discrepancy of theoretical and practically achieved artificial
intelligence in social bots: while literature reports on a high degree of intelligence
for chat bots and assumes the same for social bots, the observed degree of intelligence
in social bot implementations is limited. In fact, the overwhelming majority of
available services and software are of supportive nature and merely provide modules
of automation instead of fully fledged “intelligent” social bots. '
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Lena
full_name: Frischlich, Lena
last_name: Frischlich
- first_name: Thorsten
full_name: Quandt, Thorsten
last_name: Quandt
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Assenmacher D, Clever L, Frischlich L, Quandt T, Trautmann H, Grimme C. Demystifying
Social Bots: On the Intelligence of Automated Social Media Actors. Social Media
+ Society. 2020;6(3):2056305120939264. doi:10.1177/2056305120939264'
apa: 'Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., &
Grimme, C. (2020). Demystifying Social Bots: On the Intelligence of Automated
Social Media Actors. Social Media + Society, 6(3), 2056305120939264.
https://doi.org/10.1177/2056305120939264'
bibtex: '@article{Assenmacher_Clever_Frischlich_Quandt_Trautmann_Grimme_2020, title={Demystifying
Social Bots: On the Intelligence of Automated Social Media Actors}, volume={6},
DOI={10.1177/2056305120939264},
number={3}, journal={Social Media + Society}, author={Assenmacher, Dennis and
Clever, Lena and Frischlich, Lena and Quandt, Thorsten and Trautmann, Heike and
Grimme, Christian}, year={2020}, pages={2056305120939264} }'
chicago: 'Assenmacher, Dennis, Lena Clever, Lena Frischlich, Thorsten Quandt, Heike
Trautmann, and Christian Grimme. “Demystifying Social Bots: On the Intelligence
of Automated Social Media Actors.” Social Media + Society 6, no. 3 (2020):
2056305120939264. https://doi.org/10.1177/2056305120939264.'
ieee: 'D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, and C.
Grimme, “Demystifying Social Bots: On the Intelligence of Automated Social Media
Actors,” Social Media + Society, vol. 6, no. 3, p. 2056305120939264, 2020,
doi: 10.1177/2056305120939264.'
mla: 'Assenmacher, Dennis, et al. “Demystifying Social Bots: On the Intelligence
of Automated Social Media Actors.” Social Media + Society, vol. 6, no.
3, 2020, p. 2056305120939264, doi:10.1177/2056305120939264.'
short: D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, C. Grimme,
Social Media + Society 6 (2020) 2056305120939264.
date_created: 2023-08-04T07:41:37Z
date_updated: 2023-10-16T13:06:34Z
department:
- _id: '34'
- _id: '819'
doi: 10.1177/2056305120939264
intvolume: ' 6'
issue: '3'
language:
- iso: eng
page: '2056305120939264'
publication: Social Media + Society
status: public
title: 'Demystifying Social Bots: On the Intelligence of Automated Social Media Actors'
type: journal_article
user_id: '15504'
volume: 6
year: '2020'
...
---
_id: '46332'
abstract:
- lang: eng
text: Multimodality is one of the biggest difficulties for optimization as local
optima are often preventing algorithms from making progress. This does not only
challenge local strategies that can get stuck. It also hinders meta-heuristics
like evolutionary algorithms in convergence to the global optimum. In this paper
we present a new concept of gradient descent, which is able to escape local traps.
It relies on multiobjectivization of the original problem and applies the recently
proposed and here slightly modified multi-objective local search mechanism MOGSA.
We use a sophisticated visualization technique for multi-objective problems to
prove the working principle of our idea. As such, this work highlights the transfer
of new insights from the multi-objective to the single-objective domain and provides
first visual evidence that multiobjectivization can link single-objective local
optima in multimodal landscapes.
author:
- first_name: Vera
full_name: Steinhoff, Vera
last_name: Steinhoff
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Pelin
full_name: Aspar, Pelin
last_name: Aspar
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Steinhoff V, Kerschke P, Aspar P, Trautmann H, Grimme C. Multiobjectivization
of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient
Descent. In: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI). ; 2020:2445–2452. doi:10.1109/SSCI47803.2020.9308259'
apa: 'Steinhoff, V., Kerschke, P., Aspar, P., Trautmann, H., & Grimme, C. (2020).
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From
Multi-Objective Gradient Descent. Proceedings of the IEEE Symposium Series
on Computational Intelligence (SSCI), 2445–2452. https://doi.org/10.1109/SSCI47803.2020.9308259'
bibtex: '@inproceedings{Steinhoff_Kerschke_Aspar_Trautmann_Grimme_2020, place={Canberra,
Australia}, title={Multiobjectivization of Local Search: Single-Objective Optimization
Benefits From Multi-Objective Gradient Descent}, DOI={10.1109/SSCI47803.2020.9308259},
booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI)}, author={Steinhoff, Vera and Kerschke, Pascal and Aspar, Pelin and Trautmann,
Heike and Grimme, Christian}, year={2020}, pages={2445–2452} }'
chicago: 'Steinhoff, Vera, Pascal Kerschke, Pelin Aspar, Heike Trautmann, and Christian
Grimme. “Multiobjectivization of Local Search: Single-Objective Optimization Benefits
From Multi-Objective Gradient Descent.” In Proceedings of the IEEE Symposium
Series on Computational Intelligence (SSCI), 2445–2452. Canberra, Australia,
2020. https://doi.org/10.1109/SSCI47803.2020.9308259.'
ieee: 'V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, and C. Grimme, “Multiobjectivization
of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient
Descent,” in Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI), 2020, pp. 2445–2452, doi: 10.1109/SSCI47803.2020.9308259.'
mla: 'Steinhoff, Vera, et al. “Multiobjectivization of Local Search: Single-Objective
Optimization Benefits From Multi-Objective Gradient Descent.” Proceedings of
the IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp.
2445–2452, doi:10.1109/SSCI47803.2020.9308259.'
short: 'V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, C. Grimme, in: Proceedings
of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia,
2020, pp. 2445–2452.'
date_created: 2023-08-04T07:40:33Z
date_updated: 2023-10-16T13:05:49Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/SSCI47803.2020.9308259
language:
- iso: eng
page: 2445–2452
place: Canberra, Australia
publication: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI)
status: public
title: 'Multiobjectivization of Local Search: Single-Objective Optimization Benefits
From Multi-Objective Gradient Descent'
type: conference
user_id: '15504'
year: '2020'
...