---
_id: '61492'
abstract:
- lang: eng
text: "This paper deals with the development and results of a prediction framework
for traffic light control systems as well as the usage and benefits of such predictions
in green light optimal speed advisory (GLOSA) scenarios.\r\nVarious machine learning
methods like support vector machines, neural networks or reinforcement learning
were evaluated for their applicability in the prediction context and compared
based on their efficiency and most importantly accuracy. The resulting prediction
framework uses decision tree ensemble models combined with certain model knowledge
to forecast different control strategies. This method was chosen due to its best
performance in various test scenarios. Very high accuracy and fidelity were achieved
for standard control methods like fixed-time, time-of-day-based and 'ordinary'
traffic-based programs. Only for the more sophisticated model predictive control
which was tested lower accuracies were achieved.\r\nFor the upcoming GLOSA application
the penetration of equipped vehicles was varied for different traffic scenarios
and control strategies. Results showcase high potentials for enhancing urban mobility
and reducing environmental impact by lower emissions and waiting times. However,
it is also clear from the studies presented in this contribution that the coordination
of the control strategy with the GLOSA vehicles is of enormous importance."
author:
- first_name: Kevin
full_name: Malena, Kevin
id: '36303'
last_name: Malena
orcid: 0000-0003-1183-4679
- first_name: Christopher
full_name: Link, Christopher
id: '38249'
last_name: Link
- first_name: Sandra
full_name: Gausemeier, Sandra
id: '17793'
last_name: Gausemeier
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
citation:
ama: 'Malena K, Link C, Gausemeier S, Trächtler A. ML-based Prediction Framework
for varying Traffic Signal Control Strategies and its GLOSA-application. In: 2025
IEEE 28th International Conference on Intelligent Transportation Systems (ITSC).
Vol 28. IEEE.'
apa: Malena, K., Link, C., Gausemeier, S., & Trächtler, A. (n.d.). ML-based
Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application.
2025 IEEE 28th International Conference on Intelligent Transportation Systems
(ITSC), 28.
bibtex: '@inproceedings{Malena_Link_Gausemeier_Trächtler, title={ML-based Prediction
Framework for varying Traffic Signal Control Strategies and its GLOSA-application},
volume={28}, booktitle={2025 IEEE 28th International Conference on Intelligent
Transportation Systems (ITSC)}, publisher={IEEE}, author={Malena, Kevin and Link,
Christopher and Gausemeier, Sandra and Trächtler, Ansgar} }'
chicago: Malena, Kevin, Christopher Link, Sandra Gausemeier, and Ansgar Trächtler.
“ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and
Its GLOSA-Application.” In 2025 IEEE 28th International Conference on Intelligent
Transportation Systems (ITSC), Vol. 28. IEEE, n.d.
ieee: K. Malena, C. Link, S. Gausemeier, and A. Trächtler, “ML-based Prediction
Framework for varying Traffic Signal Control Strategies and its GLOSA-application,”
in 2025 IEEE 28th International Conference on Intelligent Transportation Systems
(ITSC), Gold Coast (Australia), vol. 28.
mla: Malena, Kevin, et al. “ML-Based Prediction Framework for Varying Traffic Signal
Control Strategies and Its GLOSA-Application.” 2025 IEEE 28th International
Conference on Intelligent Transportation Systems (ITSC), vol. 28, IEEE.
short: 'K. Malena, C. Link, S. Gausemeier, A. Trächtler, in: 2025 IEEE 28th International
Conference on Intelligent Transportation Systems (ITSC), IEEE, n.d.'
conference:
end_date: 2025-11-21
location: Gold Coast (Australia)
name: 28th International Conference on Intelligent Transportation Systems (ITSC)
start_date: 2025-11-18
date_created: 2025-10-01T11:20:34Z
date_updated: 2026-01-26T08:50:37Z
department:
- _id: '153'
intvolume: ' 28'
keyword:
- ML
- Prediction
- Tree Ensembles
- GLOSA
language:
- iso: eng
publication: 2025 IEEE 28th International Conference on Intelligent Transportation
Systems (ITSC)
publication_status: accepted
publisher: IEEE
quality_controlled: '1'
status: public
title: ML-based Prediction Framework for varying Traffic Signal Control Strategies
and its GLOSA-application
type: conference
user_id: '36303'
volume: 28
year: '2026'
...
---
_id: '62701'
abstract:
- lang: eng
text: 'Learning continuous vector representations for knowledge graphs has
significantly improved state-of-the-art performances in many challenging tasks.
Yet, deep-learning-based models are only post-hoc and locally explainable. In
contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally
explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn
Web Ontology Languages (OWLs) class expressions over large knowl-edge
graphs, while imputing missing triples. Given positive and negative example individuals,
tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL
class expression is used as a feature in a binary classification problem to represent
input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean
decision rules distinguishing positive examples from nega-tive examples. A final
OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each
positive example. By this, tDL can learn OWL class expressions without exploration,
i.e., the number of queries to a knowledge graph is bounded by the number of input
individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across
datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia
with 1.1 billion triples) show that tDL can effectively learn accurate OWL class
expressions, while the state-of-the-art models fail to return any results.
Finally, expressions learned by tDL can be seamlessly translated into
natural language explanations using a pre-trained large language model and a DL
verbalizer.'
author:
- first_name: Caglar
full_name: Demir, Caglar
last_name: Demir
- first_name: Moshood
full_name: Yekini, Moshood
last_name: Yekini
- first_name: Michael
full_name: Röder, Michael
last_name: Röder
- first_name: Yasir
full_name: Mahmood, Yasir
last_name: Mahmood
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
last_name: Ngonga Ngomo
citation:
ama: 'Demir C, Yekini M, Röder M, Mahmood Y, Ngonga Ngomo A-C. Tree-Based OWL Class
Expression Learner over Large Graphs. In: Lecture Notes in Computer Science.
Springer Nature Switzerland; 2025. doi:10.1007/978-3-032-06066-2_29'
apa: Demir, C., Yekini, M., Röder, M., Mahmood, Y., & Ngonga Ngomo, A.-C. (2025).
Tree-Based OWL Class Expression Learner over Large Graphs. In Lecture Notes
in Computer Science. European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases - ECML PKDD, Porto, Portugal.
Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-06066-2_29
bibtex: '@inbook{Demir_Yekini_Röder_Mahmood_Ngonga Ngomo_2025, place={Cham}, title={Tree-Based
OWL Class Expression Learner over Large Graphs}, DOI={10.1007/978-3-032-06066-2_29},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir
and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
chicago: 'Demir, Caglar, Moshood Yekini, Michael Röder, Yasir Mahmood, and Axel-Cyrille
Ngonga Ngomo. “Tree-Based OWL Class Expression Learner over Large Graphs.” In
Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2025.
https://doi.org/10.1007/978-3-032-06066-2_29.'
ieee: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based
OWL Class Expression Learner over Large Graphs,” in Lecture Notes in Computer
Science, Cham: Springer Nature Switzerland, 2025.'
mla: Demir, Caglar, et al. “Tree-Based OWL Class Expression Learner over Large Graphs.”
Lecture Notes in Computer Science, Springer Nature Switzerland, 2025, doi:10.1007/978-3-032-06066-2_29.
short: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, A.-C. Ngonga Ngomo, in: Lecture
Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.'
conference:
end_date: 2025-09-19
location: Porto, Portugal
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases - ECML PKDD
start_date: 2025-09-15
date_created: 2025-11-28T14:09:17Z
date_updated: 2025-11-28T14:57:39Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-032-06066-2_29
keyword:
- Decision Tree
- OWL Class Expression Learning
- Description Logic
- Knowledge Graph
- Large Language Model
- Verbalizer
language:
- iso: eng
place: Cham
project:
- _id: '285'
name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783032060655'
- '9783032060662'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Tree-Based OWL Class Expression Learner over Large Graphs
type: book_chapter
user_id: '114533'
year: '2025'
...
---
_id: '29220'
abstract:
- lang: eng
text: "Modern services often comprise several components, such as chained virtual
network functions, microservices, or\r\nmachine learning functions. Providing
such services requires to decide how often to instantiate each component, where
to place these instances in the network, how to chain them and route traffic through
them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep
reinforcement learning (DRL) approaches for self-learning network and service
management have emerged recently. These model-free DRL approaches are more flexible
but typically learn tabula rasa, i.e., disregard existing understanding of networks,
services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel
model-based AI approach that leverages existing understanding of networks and
services for more efficient and effective coordination without time-intensive
training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic
model. This allows FutureCoord to estimate the impact of future incoming traffic
and effectively optimize long-term effects, taking fluctuating demand and Quality
of Service (QoS) requirements into account. Our extensive evaluation based on
real-world network topologies, services, and traffic traces indicates that FutureCoord
clearly outperforms state-of-the-art model-free and model-based approaches with
up to 51% higher flow success ratios."
author:
- first_name: Stefan
full_name: Werner, Stefan
last_name: Werner
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination
Beyond Certainty. In: IEEE/IFIP Network Operations and Management Symposium
(NOMS). IEEE; 2022.'
apa: 'Werner, S., Schneider, S. B., & Karl, H. (2022). Use What You Know: Network
and Service Coordination Beyond Certainty. IEEE/IFIP Network Operations and
Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium
(NOMS), Budapest.'
bibtex: '@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network
and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations
and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and
Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }'
chicago: 'Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What
You Know: Network and Service Coordination Beyond Certainty.” In IEEE/IFIP
Network Operations and Management Symposium (NOMS). IEEE, 2022.'
ieee: 'S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and
Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations
and Management Symposium (NOMS), Budapest, 2022.'
mla: 'Werner, Stefan, et al. “Use What You Know: Network and Service Coordination
Beyond Certainty.” IEEE/IFIP Network Operations and Management Symposium (NOMS),
IEEE, 2022.'
short: 'S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and
Management Symposium (NOMS), IEEE, 2022.'
conference:
end_date: 2022-04-29
location: Budapest
name: IEEE/IFIP Network Operations and Management Symposium (NOMS)
start_date: 2022-04-25
date_created: 2022-01-11T08:43:26Z
date_updated: 2022-01-11T08:44:04Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2022-01-11T08:39:57Z
date_updated: 2022-01-11T08:39:57Z
file_id: '29222'
file_name: author_version.pdf
file_size: 528653
relation: main_file
file_date_updated: 2022-01-11T08:39:57Z
has_accepted_license: '1'
keyword:
- network management
- service management
- AI
- Monte Carlo Tree Search
- model-based
- QoS
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'
publication: IEEE/IFIP Network Operations and Management Symposium (NOMS)
publisher: IEEE
quality_controlled: '1'
status: public
title: 'Use What You Know: Network and Service Coordination Beyond Certainty'
type: conference
user_id: '35343'
year: '2022'
...
---
_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: '48895'
abstract:
- lang: eng
text: Evolutionary algorithms (EAs) are general-purpose problem solvers that usually
perform an unbiased search. This is reasonable and desirable in a black-box scenario.
For combinatorial optimization problems, often more knowledge about the structure
of optimal solutions is given, which can be leveraged by means of biased search
operators. We consider the Minimum Spanning Tree (MST) problem in a single- and
multi-objective version, and introduce a biased mutation, which puts more emphasis
on the selection of edges of low rank in terms of low domination number. We present
example graphs where the biased mutation can significantly speed up the expected
runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate
that bias can lead to exponential runtime if "heavy" edges are necessarily part
of an optimal solution. However, on general graphs in the single-objective setting,
we show that a combined mutation operator which decides for unbiased or biased
edge selection in each step with equal probability exhibits a polynomial upper
bound - as unbiased mutation - in the worst case and benefits from bias if the
circumstances are favorable.
author:
- first_name: Vahid
full_name: Roostapour, Vahid
last_name: Roostapour
- 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: 'Roostapour V, Bossek J, Neumann F. Runtime Analysis of Evolutionary Algorithms
with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In:
Proceedings of the 2020 Genetic and Evolutionary Computation Conference.
{GECCO} ’20. Association for Computing Machinery; 2020:551–559. doi:10.1145/3377930.3390168'
apa: Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary
Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree
Problem. Proceedings of the 2020 Genetic and Evolutionary Computation Conference,
551–559. https://doi.org/10.1145/3377930.3390168
bibtex: '@inproceedings{Roostapour_Bossek_Neumann_2020, place={New York, NY, USA},
series={{GECCO} ’20}, title={Runtime Analysis of Evolutionary Algorithms with
Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}, DOI={10.1145/3377930.3390168}, booktitle={Proceedings
of the 2020 Genetic and Evolutionary Computation Conference}, publisher={Association
for Computing Machinery}, author={Roostapour, Vahid and Bossek, Jakob and Neumann,
Frank}, year={2020}, pages={551–559}, collection={{GECCO} ’20} }'
chicago: 'Roostapour, Vahid, Jakob Bossek, and Frank Neumann. “Runtime Analysis
of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum
Spanning Tree Problem.” In Proceedings of the 2020 Genetic and Evolutionary
Computation Conference, 551–559. {GECCO} ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390168.'
ieee: 'V. Roostapour, J. Bossek, and F. Neumann, “Runtime Analysis of Evolutionary
Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree
Problem,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference,
2020, pp. 551–559, doi: 10.1145/3377930.3390168.'
mla: Roostapour, Vahid, et al. “Runtime Analysis of Evolutionary Algorithms with
Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” Proceedings
of the 2020 Genetic and Evolutionary Computation Conference, Association for
Computing Machinery, 2020, pp. 551–559, doi:10.1145/3377930.3390168.
short: 'V. Roostapour, J. Bossek, F. Neumann, in: Proceedings of the 2020 Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 551–559.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:38Z
department:
- _id: '819'
doi: 10.1145/3377930.3390168
extern: '1'
keyword:
- biased mutation
- evolutionary algorithms
- minimum spanning tree problem
- runtime analysis
language:
- iso: eng
page: 551–559
place: New York, NY, USA
publication: Proceedings of the 2020 Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publisher: Association for Computing Machinery
series_title: '{GECCO} ’20'
status: public
title: Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective
Minimum Spanning Tree Problem
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48840'
abstract:
- lang: eng
text: Research has shown that for many single-objective graph problems where optimum
solutions are composed of low weight sub-graphs, such as the minimum spanning
tree problem (MST), mutation operators favoring low weight edges show superior
performance. Intuitively, similar observations should hold for multi-criteria
variants of such problems. In this work, we focus on the multi-criteria MST problem.
A thorough experimental study is conducted where we estimate the probability of
edges being part of non-dominated spanning trees as a function of the edges’ non-domination
level or domination count, respectively. Building on gained insights, we propose
several biased one-edge-exchange mutation operators that differ in the used edge-selection
probability distribution (biased towards edges of low rank). Our empirical analysis
shows that among different graph types (dense and sparse) and edge weight types
(both uniformly random and combinations of Euclidean and uniformly random) biased
edge-selection strategies perform superior in contrast to the baseline uniform
edge-selection. Our findings are in particular strong for dense graphs.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Grimme C, Neumann F. On the Benefits of Biased Edge-Exchange Mutation
for the Multi-Criteria Spanning Tree Problem. In: Proceedings of the Genetic
and Evolutionary Computation Conference. GECCO ’19. Association for Computing
Machinery; 2019:516–523. doi:10.1145/3321707.3321818'
apa: Bossek, J., Grimme, C., & Neumann, F. (2019). On the Benefits of Biased
Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. Proceedings
of the Genetic and Evolutionary Computation Conference, 516–523. https://doi.org/10.1145/3321707.3321818
bibtex: '@inproceedings{Bossek_Grimme_Neumann_2019, place={New York, NY, USA}, series={GECCO
’19}, title={On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria
Spanning Tree Problem}, DOI={10.1145/3321707.3321818},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Neumann, Frank}, year={2019}, pages={516–523}, collection={GECCO
’19} }'
chicago: 'Bossek, Jakob, Christian Grimme, and Frank Neumann. “On the Benefits of
Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” In
Proceedings of the Genetic and Evolutionary Computation Conference, 516–523.
GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3321707.3321818.'
ieee: 'J. Bossek, C. Grimme, and F. Neumann, “On the Benefits of Biased Edge-Exchange
Mutation for the Multi-Criteria Spanning Tree Problem,” in Proceedings of the
Genetic and Evolutionary Computation Conference, 2019, pp. 516–523, doi: 10.1145/3321707.3321818.'
mla: Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for
the Multi-Criteria Spanning Tree Problem.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2019, pp. 516–523,
doi:10.1145/3321707.3321818.
short: 'J. Bossek, C. Grimme, F. Neumann, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2019, pp. 516–523.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:42:24Z
department:
- _id: '819'
doi: 10.1145/3321707.3321818
extern: '1'
keyword:
- biased mutation
- combinatorial optimization
- minimum spanning tree
- multi-objective optimization
language:
- iso: eng
page: 516–523
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-6111-8
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’19
status: public
title: On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning
Tree Problem
type: conference
user_id: '102979'
year: '2019'
...
---
_id: '17651'
abstract:
- lang: eng
text: 'Consider mitigating the effects of denial of service or of malicious traffic
in networks by deleting edges. Edge deletion reduces the DoS or the number of
the malicious flows, but it also inadvertently removes some of the desired flows.
To model this important problem, we formulate two problems: (1) remove all the
undesirable flows while minimizing the damage to the desirable ones and (2) balance
removing the undesirable flows and not removing too many of the desirable flows.
We prove these problems are equivalent to important theoretical problems, thereby
being important not only practically but also theoretically, and very hard to
approximate in a general network. We employ reductions to nonetheless approximate
the problem and also provide a greedy approximation. When the network is a tree,
the problems are still MAX SNP-hard, but we provide a greedy-based 2l-approximation
algorithm, where l is the longest desirable flow. We also provide an algorithm,
approximating the first and the second problem within {\$}{\$}2 {\backslash}sqrt{\{}
2{\backslash}left| E {\backslash}right| {\}}{\$}{\$}and {\$}{\$}2 {\backslash}sqrt{\{}2
({\backslash}left| E {\backslash}right| + {\backslash}left| {\backslash}text {\{}undesirable
flows{\}} {\backslash}right| ){\}}{\$}{\$}, respectively, where E is the set of
the edges of the network. We also provide a fixed-parameter tractable (FPT) algorithm.
Finally, if the tree has a root such that every flow in the tree flows on the
path from the root to a leaf, we solve the problem exactly using dynamic programming.'
author:
- first_name: Gleb
full_name: Polevoy, Gleb
id: '83983'
last_name: Polevoy
- first_name: Stojan
full_name: Trajanovski, Stojan
last_name: Trajanovski
- first_name: Paola
full_name: Grosso, Paola
last_name: Grosso
- first_name: Cees
full_name: de Laat, Cees
last_name: de Laat
citation:
ama: 'Polevoy G, Trajanovski S, Grosso P, de Laat C. Removing Undesirable Flows
by Edge Deletion. In: Kim D, Uma RN, Zelikovsky A, eds. Combinatorial Optimization
and Applications. Cham: Springer International Publishing; 2018:217-232.'
apa: 'Polevoy, G., Trajanovski, S., Grosso, P., & de Laat, C. (2018). Removing
Undesirable Flows by Edge Deletion. In D. Kim, R. N. Uma, & A. Zelikovsky
(Eds.), Combinatorial Optimization and Applications (pp. 217–232). Cham:
Springer International Publishing.'
bibtex: '@inproceedings{Polevoy_Trajanovski_Grosso_de Laat_2018, place={Cham}, title={Removing
Undesirable Flows by Edge Deletion}, booktitle={Combinatorial Optimization and
Applications}, publisher={Springer International Publishing}, author={Polevoy,
Gleb and Trajanovski, Stojan and Grosso, Paola and de Laat, Cees}, editor={Kim,
Donghyun and Uma, R. N. and Zelikovsky, AlexanderEditors}, year={2018}, pages={217–232}
}'
chicago: 'Polevoy, Gleb, Stojan Trajanovski, Paola Grosso, and Cees de Laat. “Removing
Undesirable Flows by Edge Deletion.” In Combinatorial Optimization and Applications,
edited by Donghyun Kim, R. N. Uma, and Alexander Zelikovsky, 217–32. Cham: Springer
International Publishing, 2018.'
ieee: G. Polevoy, S. Trajanovski, P. Grosso, and C. de Laat, “Removing Undesirable
Flows by Edge Deletion,” in Combinatorial Optimization and Applications,
2018, pp. 217–232.
mla: Polevoy, Gleb, et al. “Removing Undesirable Flows by Edge Deletion.” Combinatorial
Optimization and Applications, edited by Donghyun Kim et al., Springer International
Publishing, 2018, pp. 217–32.
short: 'G. Polevoy, S. Trajanovski, P. Grosso, C. de Laat, in: D. Kim, R.N. Uma,
A. Zelikovsky (Eds.), Combinatorial Optimization and Applications, Springer International
Publishing, Cham, 2018, pp. 217–232.'
date_created: 2020-08-06T15:19:36Z
date_updated: 2022-01-06T06:53:16Z
department:
- _id: '63'
- _id: '541'
editor:
- first_name: Donghyun
full_name: Kim, Donghyun
last_name: Kim
- first_name: R. N.
full_name: Uma, R. N.
last_name: Uma
- first_name: Alexander
full_name: Zelikovsky, Alexander
last_name: Zelikovsky
extern: '1'
keyword:
- flow
- Red-Blue Set Cover
- Positive-Negative Partial Set Cover
- approximation
- tree
- MAX SNP-hard
- root
- leaf
- dynamic programming
- FPT
language:
- iso: eng
page: 217-232
place: Cham
publication: Combinatorial Optimization and Applications
publication_identifier:
isbn:
- 978-3-030-04651-4
publisher: Springer International Publishing
status: public
title: Removing Undesirable Flows by Edge Deletion
type: conference
user_id: '83983'
year: '2018'
...
---
_id: '10598'
abstract:
- lang: eng
text: "Approximate computing has become a very popular design\r\nstrategy that exploits
error resilient computations to achieve higher\r\nperformance and energy efficiency.
Automated synthesis of approximate\r\ncircuits is performed via functional approximation,
in which various\r\nparts of the target circuit are extensively examined with
a library\r\nof approximate components/transformations to trade off the functional\r\naccuracy
and computational budget (i.e., power). However, as the number\r\nof possible
approximate transformations increases, traditional search\r\ntechniques suffer
from a combinatorial explosion due to the large\r\nbranching factor. In this work,
we present a comprehensive framework\r\nfor automated synthesis of approximate
circuits from either structural\r\nor behavioral descriptions. We adapt the Monte
Carlo Tree Search\r\n(MCTS), as a stochastic search technique, to deal with the
large design\r\nspace exploration, which enables a broader range of potential
possible\r\napproximations through lightweight random simulations. The proposed\r\nframework
is able to recognize the design Pareto set even with low\r\ncomputational budgets.
Experimental results highlight the capabilities of\r\nthe proposed synthesis framework
by resulting in up to 61.69% energy\r\nsaving while maintaining the predefined
quality constraints."
author:
- first_name: Muhammad
full_name: Awais, Muhammad
id: '64665'
last_name: Awais
orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Hassan
full_name: Ghasemzadeh Mohammadi, Hassan
id: '61186'
last_name: Ghasemzadeh Mohammadi
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
citation:
ama: 'Awais M, Ghasemzadeh Mohammadi H, Platzner M. An MCTS-based Framework for
Synthesis of Approximate Circuits. In: 26th IFIP/IEEE International Conference
on Very Large Scale Integration (VLSI-SoC). ; 2018:219-224. doi:10.1109/VLSI-SoC.2018.8645026'
apa: Awais, M., Ghasemzadeh Mohammadi, H., & Platzner, M. (2018). An MCTS-based
Framework for Synthesis of Approximate Circuits. In 26th IFIP/IEEE International
Conference on Very Large Scale Integration (VLSI-SoC) (pp. 219–224). https://doi.org/10.1109/VLSI-SoC.2018.8645026
bibtex: '@inproceedings{Awais_Ghasemzadeh Mohammadi_Platzner_2018, title={An MCTS-based
Framework for Synthesis of Approximate Circuits}, DOI={10.1109/VLSI-SoC.2018.8645026},
booktitle={26th IFIP/IEEE International Conference on Very Large Scale Integration
(VLSI-SoC)}, author={Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner,
Marco}, year={2018}, pages={219–224} }'
chicago: Awais, Muhammad, Hassan Ghasemzadeh Mohammadi, and Marco Platzner. “An
MCTS-Based Framework for Synthesis of Approximate Circuits.” In 26th IFIP/IEEE
International Conference on Very Large Scale Integration (VLSI-SoC), 219–24,
2018. https://doi.org/10.1109/VLSI-SoC.2018.8645026.
ieee: M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “An MCTS-based Framework
for Synthesis of Approximate Circuits,” in 26th IFIP/IEEE International Conference
on Very Large Scale Integration (VLSI-SoC), 2018, pp. 219–224.
mla: Awais, Muhammad, et al. “An MCTS-Based Framework for Synthesis of Approximate
Circuits.” 26th IFIP/IEEE International Conference on Very Large Scale Integration
(VLSI-SoC), 2018, pp. 219–24, doi:10.1109/VLSI-SoC.2018.8645026.
short: 'M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: 26th IFIP/IEEE International
Conference on Very Large Scale Integration (VLSI-SoC), 2018, pp. 219–224.'
date_created: 2019-07-10T09:21:38Z
date_updated: 2022-01-06T06:50:46Z
department:
- _id: '78'
doi: 10.1109/VLSI-SoC.2018.8645026
keyword:
- Approximate computing
- High-level synthesis
- Accuracy
- Monte-Carlo tree search
- Circuit simulation
language:
- iso: eng
page: 219-224
publication: 26th IFIP/IEEE International Conference on Very Large Scale Integration
(VLSI-SoC)
status: public
title: An MCTS-based Framework for Synthesis of Approximate Circuits
type: conference
user_id: '64665'
year: '2018'
...
---
_id: '37037'
abstract:
- lang: eng
text: Today we can identify a big gap between requirement specification and the
generation of test environments. This article extends the Classification Tree
Method for Embedded Systems (CTM/ES) to fill this gap by new concepts for the
precise specification of stimuli for operational ranges of continuous control
systems. It introduces novel means for continuous acceptance criteria definition
and for functional coverage definition.
author:
- first_name: Alexander
full_name: Krupp, Alexander
last_name: Krupp
- first_name: Wolfgang
full_name: Müller, Wolfgang
id: '16243'
last_name: Müller
citation:
ama: 'Krupp A, Müller W. A Systematic Approach to Combined HW/SW System Test. In:
Proceedings of DATE’10. IEEE; 2010. doi:10.1109/DATE.2010.5457186'
apa: Krupp, A., & Müller, W. (2010). A Systematic Approach to Combined HW/SW
System Test. Proceedings of DATE’10. Design, Automation & Test in Europe
Conference & Exhibition (DATE 2010), Dresden. https://doi.org/10.1109/DATE.2010.5457186
bibtex: '@inproceedings{Krupp_Müller_2010, place={Dresden}, title={A Systematic
Approach to Combined HW/SW System Test}, DOI={10.1109/DATE.2010.5457186},
booktitle={Proceedings of DATE’10}, publisher={IEEE}, author={Krupp, Alexander
and Müller, Wolfgang}, year={2010} }'
chicago: 'Krupp, Alexander, and Wolfgang Müller. “A Systematic Approach to Combined
HW/SW System Test.” In Proceedings of DATE’10. Dresden: IEEE, 2010. https://doi.org/10.1109/DATE.2010.5457186.'
ieee: 'A. Krupp and W. Müller, “A Systematic Approach to Combined HW/SW System Test,”
presented at the Design, Automation & Test in Europe Conference & Exhibition
(DATE 2010), Dresden, 2010, doi: 10.1109/DATE.2010.5457186.'
mla: Krupp, Alexander, and Wolfgang Müller. “A Systematic Approach to Combined HW/SW
System Test.” Proceedings of DATE’10, IEEE, 2010, doi:10.1109/DATE.2010.5457186.
short: 'A. Krupp, W. Müller, in: Proceedings of DATE’10, IEEE, Dresden, 2010.'
conference:
location: Dresden
name: Design, Automation & Test in Europe Conference & Exhibition (DATE 2010)
date_created: 2023-01-17T10:41:15Z
date_updated: 2023-01-17T10:41:25Z
department:
- _id: '672'
doi: 10.1109/DATE.2010.5457186
keyword:
- System testing
- Automatic testing
- Object oriented modeling
- Classification tree analysis
- Automotive engineering
- Mathematical model
- Embedded system
- Control systems
- Electronic equipment testing
- Software testing
language:
- iso: eng
place: Dresden
publication: Proceedings of DATE’10
publisher: IEEE
status: public
title: A Systematic Approach to Combined HW/SW System Test
type: conference
user_id: '5786'
year: '2010'
...
---
_id: '38784'
abstract:
- lang: eng
text: This article presents the classification tree method for functional verification
to close the gap from the specification of a test plan to SystemVerilog (Chandra
and Chakrabarty, 2001) test bench generation. Our method supports the systematic
development of test configurations and is based on the classification tree method
for embedded systems (CTM/ES) (Chakrabarty et al., 2000) extending CTM/ES for
random test generation as well as for functional coverage and property specification
author:
- first_name: Alexander
full_name: Krupp, Alexander
last_name: Krupp
- first_name: Wolfgang
full_name: Müller, Wolfgang
id: '16243'
last_name: Müller
citation:
ama: 'Krupp A, Müller W. Classification Trees for Functional Coverage and Random
Test Generation. In: Proceedings of the Design Automation & Test in Europe
Conference. IEEE; 2006. doi:10.1109/DATE.2006.243902'
apa: Krupp, A., & Müller, W. (2006). Classification Trees for Functional Coverage
and Random Test Generation. Proceedings of the Design Automation & Test
in Europe Conference. https://doi.org/10.1109/DATE.2006.243902
bibtex: '@inproceedings{Krupp_Müller_2006, place={Munich, Germany}, title={Classification
Trees for Functional Coverage and Random Test Generation}, DOI={10.1109/DATE.2006.243902},
booktitle={Proceedings of the Design Automation & Test in Europe Conference},
publisher={IEEE}, author={Krupp, Alexander and Müller, Wolfgang}, year={2006}
}'
chicago: 'Krupp, Alexander, and Wolfgang Müller. “Classification Trees for Functional
Coverage and Random Test Generation.” In Proceedings of the Design Automation
& Test in Europe Conference. Munich, Germany: IEEE, 2006. https://doi.org/10.1109/DATE.2006.243902.'
ieee: 'A. Krupp and W. Müller, “Classification Trees for Functional Coverage and
Random Test Generation,” 2006, doi: 10.1109/DATE.2006.243902.'
mla: Krupp, Alexander, and Wolfgang Müller. “Classification Trees for Functional
Coverage and Random Test Generation.” Proceedings of the Design Automation
& Test in Europe Conference, IEEE, 2006, doi:10.1109/DATE.2006.243902.
short: 'A. Krupp, W. Müller, in: Proceedings of the Design Automation & Test
in Europe Conference, IEEE, Munich, Germany, 2006.'
date_created: 2023-01-24T08:06:09Z
date_updated: 2023-01-24T08:06:14Z
department:
- _id: '672'
doi: 10.1109/DATE.2006.243902
keyword:
- Classification tree analysis
- System testing
- Embedded system
- Safety
- Automatic testing
- Automation
language:
- iso: eng
place: Munich, Germany
publication: Proceedings of the Design Automation & Test in Europe Conference
publication_identifier:
isbn:
- 3-9810801-1-4
publisher: IEEE
status: public
title: Classification Trees for Functional Coverage and Random Test Generation
type: conference
user_id: '5786'
year: '2006'
...