---
_id: '46318'
abstract:
- lang: eng
text: 'Multi-objective (MO) optimization, i.e., the simultaneous optimization of
multiple conflicting objectives, is gaining more and more attention in various
research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter
optimization), or logistics (e.g., vehicle routing). Many works in this domain
mention the structural problem property of multimodality as a challenge from two
classical perspectives: (1) finding all globally optimal solution sets, and (2)
avoiding to get trapped in local optima. Interestingly, these streams seem to
transfer many traditional concepts of single-objective (SO) optimization into
claims, assumptions, or even terminology regarding the MO domain, but mostly neglect
the understanding of the structural properties as well as the algorithmic search
behavior on a problem’s landscape. However, some recent works counteract this
trend, by investigating the fundamentals and characteristics of MO problems using
new visualization techniques and gaining surprising insights. Using these visual
insights, this work proposes a step towards a unified terminology to capture multimodality
and locality in a broader way than it is usually done. This enables us to investigate
current research activities in multimodal continuous MO optimization and to highlight
new implications and promising research directions for the design of benchmark
suites, the discovery of MO landscape features, the development of new MO (or
even SO) optimization algorithms, and performance indicators. For all these topics,
we provide a review of ideas and methods but also an outlook on future challenges,
research potential and perspectives that result from recent developments.'
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- 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: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: André H.
full_name: Deutz, André H.
last_name: Deutz
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Michael
full_name: Emmerich, Michael
last_name: Emmerich
citation:
ama: 'Grimme C, Kerschke P, Aspar P, et al. Peeking beyond peaks: Challenges and
research potentials of continuous multimodal multi-objective optimization. Computers
& Operations Research. 2021;136:105489. doi:https://doi.org/10.1016/j.cor.2021.105489'
apa: 'Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A.
H., Wang, H., & Emmerich, M. (2021). Peeking beyond peaks: Challenges and
research potentials of continuous multimodal multi-objective optimization. Computers
& Operations Research, 136, 105489. https://doi.org/10.1016/j.cor.2021.105489'
bibtex: '@article{Grimme_Kerschke_Aspar_Trautmann_Preuss_Deutz_Wang_Emmerich_2021,
title={Peeking beyond peaks: Challenges and research potentials of continuous
multimodal multi-objective optimization}, volume={136}, DOI={https://doi.org/10.1016/j.cor.2021.105489},
journal={Computers & Operations Research}, author={Grimme, Christian and Kerschke,
Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André
H. and Wang, Hao and Emmerich, Michael}, year={2021}, pages={105489} }'
chicago: 'Grimme, Christian, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Mike
Preuss, André H. Deutz, Hao Wang, and Michael Emmerich. “Peeking beyond Peaks:
Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.”
Computers & Operations Research 136 (2021): 105489. https://doi.org/10.1016/j.cor.2021.105489.'
ieee: 'C. Grimme et al., “Peeking beyond peaks: Challenges and research potentials
of continuous multimodal multi-objective optimization,” Computers & Operations
Research, vol. 136, p. 105489, 2021, doi: https://doi.org/10.1016/j.cor.2021.105489.'
mla: 'Grimme, Christian, et al. “Peeking beyond Peaks: Challenges and Research Potentials
of Continuous Multimodal Multi-Objective Optimization.” Computers & Operations
Research, vol. 136, 2021, p. 105489, doi:https://doi.org/10.1016/j.cor.2021.105489.'
short: C. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A.H. Deutz, H.
Wang, M. Emmerich, Computers & Operations Research 136 (2021) 105489.
date_created: 2023-08-04T07:28:34Z
date_updated: 2023-10-16T12:58:42Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1016/j.cor.2021.105489
intvolume: ' 136'
keyword:
- Multimodal optimization
- Multi-objective continuous optimization
- Landscape analysis
- Visualization
- Benchmarking
- Theory
- Algorithms
language:
- iso: eng
page: '105489'
publication: Computers & Operations Research
publication_identifier:
issn:
- 0305-0548
status: public
title: 'Peeking beyond peaks: Challenges and research potentials of continuous multimodal
multi-objective optimization'
type: journal_article
user_id: '15504'
volume: 136
year: '2021'
...
---
_id: '46311'
abstract:
- lang: eng
text: "In this work we examine the inner mechanisms of the recently developed sophisticated
local search procedure SOMOGSA. This method solves multimodal single-objective
continuous optimization problems by first expanding the problem with an additional
objective (e.g., a sphere function) to the bi-objective space, and subsequently
exploiting local structures and ridges of the resulting landscapes. Our study
particularly focusses on the sensitivity of this multiobjectivization approach
w.r.t. (i) the parametrization of the artificial second objective, as well as
(ii) the position of the initial starting points in the search space.\r\n\r\nAs
SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient
and Nelder-Mead local search (as optimizers in the respective module) and compare
the performance of the resulting hybrid local search to their original single-objective
counterparts. We show that the SOMOGSA framework can significantly boost local
search by multiobjectivization. Combined with more sophisticated local search
and metaheuristics this may help in solving highly multimodal optimization problems
in future."
author:
- first_name: Pelin
full_name: Aspar, Pelin
last_name: Aspar
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Vera
full_name: Steinhoff, Vera
last_name: Steinhoff
- 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: 'Aspar P, Kerschke P, Steinhoff V, Trautmann H, Grimme C. Multi^3: Optimizing
Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by
Means of Multiobjectivization. In: et al. Ishibuchi H, ed. Evolutionary Multi-Criterion
Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March
28–31, 2021, Proceedings. Springer; 2021:311–322. doi:10.1007/978-3-030-72062-9_25'
apa: 'Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., & Grimme, C. (2021).
Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective
Space by Means of Multiobjectivization. In H. et al. Ishibuchi (Ed.), Evolutionary
Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen,
China, March 28–31, 2021, Proceedings (pp. 311–322). Springer. https://doi.org/10.1007/978-3-030-72062-9_25'
bibtex: '@inproceedings{Aspar_Kerschke_Steinhoff_Trautmann_Grimme_2021, place={Heidelberg,
Berlin}, title={Multi^3: Optimizing Multimodal Single-Objective Continuous Problems
in the Multi-Objective Space by Means of Multiobjectivization}, DOI={10.1007/978-3-030-72062-9_25},
booktitle={Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference,
EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings}, publisher={Springer},
author={Aspar, Pelin and Kerschke, Pascal and Steinhoff, Vera and Trautmann, Heike
and Grimme, Christian}, editor={et al. Ishibuchi, H.}, year={2021}, pages={311–322}
}'
chicago: 'Aspar, Pelin, Pascal Kerschke, Vera Steinhoff, Heike Trautmann, and Christian
Grimme. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in
the Multi-Objective Space by Means of Multiobjectivization.” In Evolutionary
Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen,
China, March 28–31, 2021, Proceedings, edited by H. et al. Ishibuchi, 311–322.
Heidelberg, Berlin: Springer, 2021. https://doi.org/10.1007/978-3-030-72062-9_25.'
ieee: 'P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, and C. Grimme, “Multi^3:
Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective
Space by Means of Multiobjectivization,” in Evolutionary Multi-Criterion Optimization:
11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021,
Proceedings, 2021, pp. 311–322, doi: 10.1007/978-3-030-72062-9_25.'
mla: 'Aspar, Pelin, et al. “Multi^3: Optimizing Multimodal Single-Objective Continuous
Problems in the Multi-Objective Space by Means of Multiobjectivization.” Evolutionary
Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen,
China, March 28–31, 2021, Proceedings, edited by H. et al. Ishibuchi, Springer,
2021, pp. 311–322, doi:10.1007/978-3-030-72062-9_25.'
short: 'P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, C. Grimme, in: H. et
al. Ishibuchi (Ed.), Evolutionary Multi-Criterion Optimization: 11$^th$ International
Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, Springer,
Heidelberg, Berlin, 2021, pp. 311–322.'
date_created: 2023-08-04T07:21:17Z
date_updated: 2023-10-16T12:54:29Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-72062-9_25
editor:
- first_name: H.
full_name: et al. Ishibuchi, H.
last_name: et al. Ishibuchi
language:
- iso: eng
page: 311–322
place: Heidelberg, Berlin
publication: 'Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference,
EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings'
publisher: Springer
status: public
title: 'Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the
Multi-Objective Space by Means of Multiobjectivization'
type: conference
user_id: '15504'
year: '2021'
...
---
_id: '46317'
abstract:
- lang: eng
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.'
author:
- first_name: Crispin
full_name: Coombs, Crispin
last_name: Coombs
- first_name: Patrick
full_name: Stacey, Patrick
last_name: Stacey
- first_name: Peter
full_name: Kawalek, Peter
last_name: Kawalek
- first_name: Boyka
full_name: Simeonova, Boyka
last_name: Simeonova
- first_name: Jörg
full_name: Becker, Jörg
last_name: Becker
- first_name: Katrin
full_name: Bergener, Katrin
last_name: Bergener
- first_name: João Álvaro
full_name: Carvalho, João Álvaro
last_name: Carvalho
- first_name: Marcelo
full_name: Fantinato, Marcelo
last_name: Fantinato
- first_name: Niels F.
full_name: Garmann-Johnsen, Niels F.
last_name: Garmann-Johnsen
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Armin
full_name: Stein, Armin
last_name: Stein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
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. International Journal
of Information Management. 2021;58. doi:10.1016/j.ijinfomgt.2021.102311
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.,
& Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away
To Intelligent Machines? A European Perspective. International Journal of Information
Management, 58. https://doi.org/10.1016/j.ijinfomgt.2021.102311
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={10.1016/j.ijinfomgt.2021.102311},
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} }'
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.” International
Journal of Information Management 58 (2021). https://doi.org/10.1016/j.ijinfomgt.2021.102311.
ieee: 'C. Coombs et al., “What Is It About Humanity That We Can’t Give Away
To Intelligent Machines? A European Perspective,” International Journal of
Information Management, vol. 58, 2021, doi: 10.1016/j.ijinfomgt.2021.102311.'
mla: Coombs, Crispin, et al. “What Is It About Humanity That We Can’t Give Away
To Intelligent Machines? A European Perspective.” International Journal of
Information Management, vol. 58, 2021, doi:10.1016/j.ijinfomgt.2021.102311.
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).
date_created: 2023-08-04T07:27:14Z
date_updated: 2023-10-16T12:58:02Z
department:
- _id: '34'
- _id: '819'
doi: 10.1016/j.ijinfomgt.2021.102311
intvolume: ' 58'
language:
- iso: eng
publication: International Journal of Information Management
status: public
title: What Is It About Humanity That We Can’t Give Away To Intelligent Machines?
A European Perspective
type: journal_article
user_id: '15504'
volume: 58
year: '2021'
...
---
_id: '48853'
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.
author:
- 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: 'Bossek J, Neumann A, Neumann F. Breeding Diverse Packings for the Knapsack
Problem by Means of Diversity-Tailored Evolutionary Algorithms. In: Proceedings
of the Genetic and Evolutionary Computation Conference. GECCO ’21. Association
for Computing Machinery; 2021:556–564. doi:10.1145/3449639.3459364'
apa: Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding Diverse Packings
for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.
Proceedings of the Genetic and Evolutionary Computation Conference, 556–564.
https://doi.org/10.1145/3449639.3459364
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={10.1145/3449639.3459364},
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}
}'
chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Breeding Diverse Packings
for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
556–564. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021.
https://doi.org/10.1145/3449639.3459364.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the
Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2021, pp. 556–564,
doi: 10.1145/3449639.3459364.'
mla: Bossek, Jakob, et al. “Breeding Diverse Packings for the Knapsack Problem by
Means of Diversity-Tailored Evolutionary Algorithms.” Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
2021, pp. 556–564, doi:10.1145/3449639.3459364.
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.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:45:22Z
department:
- _id: '819'
doi: 10.1145/3449639.3459364
extern: '1'
keyword:
- evolutionary algorithms
- evolutionary diversity optimization
- knapsack problem
- tailored operators
language:
- iso: eng
page: 556–564
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-8350-9
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’21
status: public
title: Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored
Evolutionary Algorithms
type: conference
user_id: '102979'
year: '2021'
...
---
_id: '48855'
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.
author:
- 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: 'Bossek J, Neumann A, Neumann F. Exact Counting and~Sampling of Optima for
the Knapsack Problem. In: Learning and Intelligent Optimization. Springer-Verlag;
2021:40–54. doi:10.1007/978-3-030-92121-7_4'
apa: Bossek, J., Neumann, A., & Neumann, F. (2021). Exact Counting and~Sampling
of Optima for the Knapsack Problem. Learning and Intelligent Optimization,
40–54. https://doi.org/10.1007/978-3-030-92121-7_4
bibtex: '@inproceedings{Bossek_Neumann_Neumann_2021, place={Berlin, Heidelberg},
title={Exact Counting and~Sampling of Optima for the Knapsack Problem}, DOI={10.1007/978-3-030-92121-7_4},
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 Learning and Intelligent Optimization,
40–54. Berlin, Heidelberg: Springer-Verlag, 2021. https://doi.org/10.1007/978-3-030-92121-7_4.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Exact Counting and~Sampling of Optima
for the Knapsack Problem,” in Learning and Intelligent Optimization, 2021,
pp. 40–54, doi: 10.1007/978-3-030-92121-7_4.'
mla: Bossek, Jakob, et al. “Exact Counting And~Sampling of Optima for the Knapsack
Problem.” Learning and Intelligent Optimization, Springer-Verlag, 2021,
pp. 40–54, doi:10.1007/978-3-030-92121-7_4.
short: 'J. Bossek, A. Neumann, F. Neumann, in: Learning and Intelligent Optimization,
Springer-Verlag, Berlin, Heidelberg, 2021, pp. 40–54.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:45:14Z
department:
- _id: '819'
doi: 10.1007/978-3-030-92121-7_4
extern: '1'
keyword:
- Dynamic programming
- Exact counting
- Sampling
- Zero-one knapsack problem
language:
- iso: eng
page: 40–54
place: Berlin, Heidelberg
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-030-92120-0
publication_status: published
publisher: Springer-Verlag
status: public
title: Exact Counting and~Sampling of Optima for the Knapsack Problem
type: conference
user_id: '102979'
year: '2021'
...
---
_id: '48860'
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.
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
citation:
ama: 'Bossek J, Neumann F. Evolutionary Diversity Optimization and the Minimum Spanning
Tree Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’21. Association for Computing Machinery; 2021:198–206. doi:10.1145/3449639.3459363'
apa: Bossek, J., & Neumann, F. (2021). Evolutionary Diversity Optimization and
the Minimum Spanning Tree Problem. Proceedings of the Genetic and Evolutionary
Computation Conference, 198–206. https://doi.org/10.1145/3449639.3459363
bibtex: '@inproceedings{Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO
’21}, title={Evolutionary Diversity Optimization and the Minimum Spanning Tree
Problem}, DOI={10.1145/3449639.3459363},
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} }'
chicago: 'Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization
and the Minimum Spanning Tree Problem.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 198–206. GECCO ’21. New York, NY, USA: Association
for Computing Machinery, 2021. https://doi.org/10.1145/3449639.3459363.'
ieee: 'J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum
Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2021, pp. 198–206, doi: 10.1145/3449639.3459363.'
mla: Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and
the Minimum Spanning Tree Problem.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2021, pp. 198–206,
doi:10.1145/3449639.3459363.
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.'
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:45:37Z
department:
- _id: '819'
doi: 10.1145/3449639.3459363
extern: '1'
keyword:
- evolutionary algorithms
- evolutionary diversity optimization
- minimum spanning tree
- runtime analysis
language:
- iso: eng
page: 198–206
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-8350-9
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’21
status: public
title: Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem
type: conference
user_id: '102979'
year: '2021'
...
---
_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'
...