---
_id: '22463'
author:
- first_name: Linghui
full_name: Luo, Linghui
last_name: Luo
- first_name: Martin
full_name: Schäf, Martin
last_name: Schäf
- first_name: Daniel
full_name: Sanchez, Daniel
last_name: Sanchez
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Luo L, Schäf M, Sanchez D, Bodden E. IDE Support for Cloud-Based Static Analyses.
In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering
Conference and Symposium on the Foundations of Software Engineering. ; 2021.'
apa: Luo, L., Schäf, M., Sanchez, D., & Bodden, E. (2021). IDE Support for Cloud-Based
Static Analyses. Proceedings of the 29th ACM Joint Meeting on European Software
Engineering Conference and Symposium on the Foundations of Software Engineering.
bibtex: '@inproceedings{Luo_Schäf_Sanchez_Bodden_2021, title={IDE Support for Cloud-Based
Static Analyses}, booktitle={Proceedings of the 29th ACM Joint Meeting on European
Software Engineering Conference and Symposium on the Foundations of Software Engineering},
author={Luo, Linghui and Schäf, Martin and Sanchez, Daniel and Bodden, Eric},
year={2021} }'
chicago: Luo, Linghui, Martin Schäf, Daniel Sanchez, and Eric Bodden. “IDE Support
for Cloud-Based Static Analyses.” In Proceedings of the 29th ACM Joint Meeting
on European Software Engineering Conference and Symposium on the Foundations of
Software Engineering, 2021.
ieee: L. Luo, M. Schäf, D. Sanchez, and E. Bodden, “IDE Support for Cloud-Based
Static Analyses,” 2021.
mla: Luo, Linghui, et al. “IDE Support for Cloud-Based Static Analyses.” Proceedings
of the 29th ACM Joint Meeting on European Software Engineering Conference and
Symposium on the Foundations of Software Engineering, 2021.
short: 'L. Luo, M. Schäf, D. Sanchez, E. Bodden, in: Proceedings of the 29th ACM
Joint Meeting on European Software Engineering Conference and Symposium on the
Foundations of Software Engineering, 2021.'
date_created: 2021-06-17T10:18:05Z
date_updated: 2022-10-20T13:11:45Z
department:
- _id: '76'
language:
- iso: eng
publication: Proceedings of the 29th ACM Joint Meeting on European Software Engineering
Conference and Symposium on the Foundations of Software Engineering
status: public
title: IDE Support for Cloud-Based Static Analyses
type: conference
user_id: '15249'
year: '2021'
...
---
_id: '33840'
author:
- first_name: Kadiray
full_name: Karakaya, Kadiray
id: '70410'
last_name: Karakaya
orcid: https://orcid.org/0000-0001-9266-2084
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Karakaya K, Bodden E. SootFX: A Static Code Feature Extraction Tool for Java
and Android. In: 2021 IEEE 21st International Working Conference on Source
Code Analysis and Manipulation (SCAM). ; 2021:181–186.'
apa: 'Karakaya, K., & Bodden, E. (2021). SootFX: A Static Code Feature Extraction
Tool for Java and Android. 2021 IEEE 21st International Working Conference
on Source Code Analysis and Manipulation (SCAM), 181–186.'
bibtex: '@inproceedings{Karakaya_Bodden_2021, title={SootFX: A Static Code Feature
Extraction Tool for Java and Android}, booktitle={2021 IEEE 21st International
Working Conference on Source Code Analysis and Manipulation (SCAM)}, author={Karakaya,
Kadiray and Bodden, Eric}, year={2021}, pages={181–186} }'
chicago: 'Karakaya, Kadiray, and Eric Bodden. “SootFX: A Static Code Feature Extraction
Tool for Java and Android.” In 2021 IEEE 21st International Working Conference
on Source Code Analysis and Manipulation (SCAM), 181–186, 2021.'
ieee: 'K. Karakaya and E. Bodden, “SootFX: A Static Code Feature Extraction Tool
for Java and Android,” in 2021 IEEE 21st International Working Conference on
Source Code Analysis and Manipulation (SCAM), 2021, pp. 181–186.'
mla: 'Karakaya, Kadiray, and Eric Bodden. “SootFX: A Static Code Feature Extraction
Tool for Java and Android.” 2021 IEEE 21st International Working Conference
on Source Code Analysis and Manipulation (SCAM), 2021, pp. 181–186.'
short: 'K. Karakaya, E. Bodden, in: 2021 IEEE 21st International Working Conference
on Source Code Analysis and Manipulation (SCAM), 2021, pp. 181–186.'
date_created: 2022-10-20T13:09:08Z
date_updated: 2022-10-20T13:09:23Z
department:
- _id: '76'
language:
- iso: eng
page: 181–186
publication: 2021 IEEE 21st International Working Conference on Source Code Analysis
and Manipulation (SCAM)
status: public
title: 'SootFX: A Static Code Feature Extraction Tool for Java and Android'
type: conference
user_id: '15249'
year: '2021'
...
---
_id: '32401'
author:
- first_name: Jan Niklas
full_name: Siemer, Jan Niklas
last_name: Siemer
citation:
ama: Siemer JN. Lattice Reductions and Their Applications to Cryptographic Systems.;
2021.
apa: Siemer, J. N. (2021). Lattice reductions and their applications to cryptographic
systems.
bibtex: '@book{Siemer_2021, title={Lattice reductions and their applications to
cryptographic systems}, author={Siemer, Jan Niklas}, year={2021} }'
chicago: Siemer, Jan Niklas. Lattice Reductions and Their Applications to Cryptographic
Systems, 2021.
ieee: J. N. Siemer, Lattice reductions and their applications to cryptographic
systems. 2021.
mla: Siemer, Jan Niklas. Lattice Reductions and Their Applications to Cryptographic
Systems. 2021.
short: J.N. Siemer, Lattice Reductions and Their Applications to Cryptographic Systems,
2021.
date_created: 2022-07-20T11:40:28Z
date_updated: 2023-01-10T10:49:33Z
department:
- _id: '64'
language:
- iso: eng
status: public
supervisor:
- first_name: Johannes
full_name: Blömer, Johannes
id: '23'
last_name: Blömer
title: Lattice reductions and their applications to cryptographic systems
type: bachelorsthesis
user_id: '47434'
year: '2021'
...
---
_id: '32400'
author:
- first_name: Anonymous
full_name: Anonymous, Anonymous
last_name: Anonymous
citation:
ama: Anonymous A. Performance Analysis of FHE Libraries.; 2021.
apa: Anonymous, A. (2021). Performance Analysis of FHE Libraries.
bibtex: '@book{Anonymous_2021, title={Performance Analysis of FHE Libraries}, author={Anonymous,
Anonymous}, year={2021} }'
chicago: Anonymous, Anonymous. Performance Analysis of FHE Libraries, 2021.
ieee: A. Anonymous, Performance Analysis of FHE Libraries. 2021.
mla: Anonymous, Anonymous. Performance Analysis of FHE Libraries. 2021.
short: A. Anonymous, Performance Analysis of FHE Libraries, 2021.
date_created: 2022-07-20T11:37:41Z
date_updated: 2023-01-10T11:54:39Z
department:
- _id: '64'
language:
- iso: eng
status: public
supervisor:
- first_name: Johannes
full_name: Blömer, Johannes
id: '23'
last_name: Blömer
title: Performance Analysis of FHE Libraries
type: mastersthesis
user_id: '47434'
year: '2021'
...
---
_id: '35889'
abstract:
- lang: eng
text: Network and service coordination is important to provide modern services consisting
of multiple interconnected components, e.g., in 5G, network function virtualization
(NFV), or cloud and edge computing. In this paper, I outline my dissertation research,
which proposes six approaches to automate such network and service coordination.
All approaches dynamically react to the current demand and optimize coordination
for high service quality and low costs. The approaches range from centralized
to distributed methods and from conventional heuristic algorithms and mixed-integer
linear programs to machine learning approaches using supervised and reinforcement
learning. I briefly discuss their main ideas and advantages over other state-of-the-art
approaches and compare strengths and weaknesses.
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
citation:
ama: Schneider SB. Conventional and Machine Learning Approaches for Network and
Service Coordination.; 2021.
apa: Schneider, S. B. (2021). Conventional and Machine Learning Approaches for
Network and Service Coordination.
bibtex: '@book{Schneider_2021, title={Conventional and Machine Learning Approaches
for Network and Service Coordination}, author={Schneider, Stefan Balthasar}, year={2021}
}'
chicago: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches
for Network and Service Coordination, 2021.
ieee: S. B. Schneider, Conventional and Machine Learning Approaches for Network
and Service Coordination. 2021.
mla: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches
for Network and Service Coordination. 2021.
short: S.B. Schneider, Conventional and Machine Learning Approaches for Network
and Service Coordination, 2021.
date_created: 2023-01-10T15:08:50Z
date_updated: 2023-01-10T15:09:05Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2023-01-10T15:07:03Z
date_updated: 2023-01-10T15:07:03Z
file_id: '35890'
file_name: main.pdf
file_size: 133340
relation: main_file
file_date_updated: 2023-01-10T15:07:03Z
has_accepted_license: '1'
keyword:
- nfv
- coordination
- machine learning
- reinforcement learning
- phd
- digest
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
name: 'SFB 901 - C4: SFB 901 - Subproject C4'
status: public
title: Conventional and Machine Learning Approaches for Network and Service Coordination
type: working_paper
user_id: '35343'
year: '2021'
...
---
_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: '21272'
author:
- first_name: Patrick
full_name: Ködding, Patrick
id: '45402'
last_name: Ködding
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: 'Ködding P, Dumitrescu R. Forschungsfelder für Künstliche Intelligenz in der
strategischen Produktplanung. In: Hartmann EA, ed. Digitalisisierung souverän
gestalten. Springer Vieweg; 2021:59-73.'
apa: Ködding, P., & Dumitrescu, R. (2021). Forschungsfelder für Künstliche Intelligenz
in der strategischen Produktplanung. In E. A. Hartmann (Ed.), Digitalisisierung
souverän gestalten (pp. 59–73). Springer Vieweg.
bibtex: '@inproceedings{Ködding_Dumitrescu_2021, place={Berlin, Heidelberg}, title={Forschungsfelder
für Künstliche Intelligenz in der strategischen Produktplanung}, booktitle={Digitalisisierung
souverän gestalten}, publisher={Springer Vieweg}, author={Ködding, Patrick and
Dumitrescu, Roman}, editor={Hartmann, Ernst A.}, year={2021}, pages={59–73} }'
chicago: 'Ködding, Patrick, and Roman Dumitrescu. “Forschungsfelder für Künstliche
Intelligenz in der strategischen Produktplanung.” In Digitalisisierung souverän
gestalten, edited by Ernst A. Hartmann, 59–73. Berlin, Heidelberg: Springer
Vieweg, 2021.'
ieee: P. Ködding and R. Dumitrescu, “Forschungsfelder für Künstliche Intelligenz
in der strategischen Produktplanung,” in Digitalisisierung souverän gestalten,
2021, pp. 59–73.
mla: Ködding, Patrick, and Roman Dumitrescu. “Forschungsfelder für Künstliche Intelligenz
in der strategischen Produktplanung.” Digitalisisierung souverän gestalten,
edited by Ernst A. Hartmann, Springer Vieweg, 2021, pp. 59–73.
short: 'P. Ködding, R. Dumitrescu, in: E.A. Hartmann (Ed.), Digitalisisierung souverän
gestalten, Springer Vieweg, Berlin, Heidelberg, 2021, pp. 59–73.'
date_created: 2021-02-24T13:07:33Z
date_updated: 2023-10-06T12:32:24Z
department:
- _id: '563'
editor:
- first_name: Ernst A.
full_name: Hartmann, Ernst A.
last_name: Hartmann
language:
- iso: ger
page: pp. 59-73
place: Berlin, Heidelberg
publication: Digitalisisierung souverän gestalten
publication_status: published
publisher: Springer Vieweg
status: public
title: Forschungsfelder für Künstliche Intelligenz in der strategischen Produktplanung
type: conference
user_id: '45402'
year: '2021'
...
---
_id: '48286'
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Habernal I. When differential privacy meets NLP: The devil is in the detail.
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language
Processing. Association for Computational Linguistics; 2021. doi:10.18653/v1/2021.emnlp-main.114'
apa: 'Habernal, I. (2021). When differential privacy meets NLP: The devil is in
the detail. Proceedings of the 2021 Conference on Empirical Methods in Natural
Language Processing. https://doi.org/10.18653/v1/2021.emnlp-main.114'
bibtex: '@inproceedings{Habernal_2021, title={When differential privacy meets NLP:
The devil is in the detail}, DOI={10.18653/v1/2021.emnlp-main.114},
booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural
Language Processing}, publisher={Association for Computational Linguistics}, author={Habernal,
Ivan}, year={2021} }'
chicago: 'Habernal, Ivan. “When Differential Privacy Meets NLP: The Devil Is in
the Detail.” In Proceedings of the 2021 Conference on Empirical Methods in
Natural Language Processing. Association for Computational Linguistics, 2021.
https://doi.org/10.18653/v1/2021.emnlp-main.114.'
ieee: 'I. Habernal, “When differential privacy meets NLP: The devil is in the detail,”
2021, doi: 10.18653/v1/2021.emnlp-main.114.'
mla: 'Habernal, Ivan. “When Differential Privacy Meets NLP: The Devil Is in the
Detail.” Proceedings of the 2021 Conference on Empirical Methods in Natural
Language Processing, Association for Computational Linguistics, 2021, doi:10.18653/v1/2021.emnlp-main.114.'
short: 'I. Habernal, in: Proceedings of the 2021 Conference on Empirical Methods
in Natural Language Processing, Association for Computational Linguistics, 2021.'
date_created: 2023-10-19T08:21:43Z
date_updated: 2023-10-19T12:04:40Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2021.emnlp-main.114
language:
- iso: eng
publication: Proceedings of the 2021 Conference on Empirical Methods in Natural Language
Processing
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: 'When differential privacy meets NLP: The devil is in the detail'
type: conference
user_id: '15504'
year: '2021'
...
---
_id: '24456'
abstract:
- lang: eng
text: One objective of current research in explainable intelligent systems is to
implement social aspects in order to increase the relevance of explanations. In
this paper, we argue that a novel conceptual framework is needed to overcome shortcomings
of existing AI systems with little attention to processes of interaction and learning.
Drawing from research in interaction and development, we first outline the novel
conceptual framework that pushes the design of AI systems toward true interactivity
with an emphasis on the role of the partner and social relevance. We propose that
AI systems will be able to provide a meaningful and relevant explanation only
if the process of explaining is extended to active contribution of both partners
that brings about dynamics that is modulated by different levels of analysis.
Accordingly, our conceptual framework comprises monitoring and scaffolding as
key concepts and claims that the process of explaining is not only modulated by
the interaction between explainee and explainer but is embedded into a larger
social context in which conventionalized and routinized behaviors are established.
We discuss our conceptual framework in relation to the established objectives
of transparency and autonomy that are raised for the design of explainable AI
systems currently.
article_type: original
author:
- first_name: Katharina J.
full_name: Rohlfing, Katharina J.
id: '50352'
last_name: Rohlfing
- first_name: Philipp
full_name: Cimiano, Philipp
last_name: Cimiano
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Tobias
full_name: Matzner, Tobias
id: '65695'
last_name: Matzner
- first_name: Heike M.
full_name: Buhl, Heike M.
id: '27152'
last_name: Buhl
- first_name: Hendrik
full_name: Buschmeier, Hendrik
last_name: Buschmeier
- first_name: Elena
full_name: Esposito, Elena
last_name: Esposito
- first_name: Angela
full_name: Grimminger, Angela
id: '57578'
last_name: Grimminger
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Ilona
full_name: Horwath, Ilona
id: '68836'
last_name: Horwath
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Friederike
full_name: Kern, Friederike
last_name: Kern
- first_name: Stefan
full_name: Kopp, Stefan
last_name: Kopp
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: 'Rohlfing KJ, Cimiano P, Scharlau I, et al. Explanation as a Social Practice:
Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems. 2021;13(3):717-728. doi:10.1109/tcds.2020.3044366'
apa: 'Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier,
H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier,
E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth,
H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward
a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems, 13(3), 717–728. https://doi.org/10.1109/tcds.2020.3044366'
bibtex: '@article{Rohlfing_Cimiano_Scharlau_Matzner_Buhl_Buschmeier_Esposito_Grimminger_Hammer_Haeb-Umbach_et
al._2021, title={Explanation as a Social Practice: Toward a Conceptual Framework
for the Social Design of AI Systems}, volume={13}, DOI={10.1109/tcds.2020.3044366},
number={3}, journal={IEEE Transactions on Cognitive and Developmental Systems},
author={Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner,
Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger,
Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and et al.}, year={2021},
pages={717–728} }'
chicago: 'Rohlfing, Katharina J., Philipp Cimiano, Ingrid Scharlau, Tobias Matzner,
Heike M. Buhl, Hendrik Buschmeier, Elena Esposito, et al. “Explanation as a Social
Practice: Toward a Conceptual Framework for the Social Design of AI Systems.”
IEEE Transactions on Cognitive and Developmental Systems 13, no. 3 (2021):
717–28. https://doi.org/10.1109/tcds.2020.3044366.'
ieee: 'K. J. Rohlfing et al., “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems,” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 717–728, 2021,
doi: 10.1109/tcds.2020.3044366.'
mla: 'Rohlfing, Katharina J., et al. “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems.” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, 2021, pp. 717–28,
doi:10.1109/tcds.2020.3044366.'
short: K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier,
E. Esposito, A. Grimminger, B. Hammer, R. Haeb-Umbach, I. Horwath, E. Hüllermeier,
F. Kern, S. Kopp, K. Thommes, A.-C. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P.
Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems 13
(2021) 717–728.
date_created: 2021-09-14T20:52:57Z
date_updated: 2023-12-05T10:15:02Z
ddc:
- '300'
department:
- _id: '603'
- _id: '749'
- _id: '424'
- _id: '67'
- _id: '574'
- _id: '184'
- _id: '757'
- _id: '54'
- _id: '178'
doi: 10.1109/tcds.2020.3044366
file:
- access_level: open_access
content_type: application/pdf
creator: haebumb
date_created: 2023-11-20T16:33:51Z
date_updated: 2023-11-20T16:33:51Z
file_id: '49081'
file_name: 2020-12-01_explainability_final_version.pdf
file_size: 626217
relation: main_file
file_date_updated: 2023-11-20T16:33:51Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '3'
keyword:
- Explainability
- process ofexplaining andunderstanding
- explainable artificial systems
language:
- iso: eng
oa: '1'
page: 717-728
project:
- _id: '109'
grant_number: '438445824'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: IEEE Transactions on Cognitive and Developmental Systems
publication_identifier:
issn:
- 2379-8920
- 2379-8939
publication_status: published
quality_controlled: '1'
status: public
title: 'Explanation as a Social Practice: Toward a Conceptual Framework for the Social
Design of AI Systems'
type: journal_article
user_id: '42933'
volume: 13
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'
...