---
_id: '46345'
abstract:
- lang: eng
text: It has long been observed that for practically any computational problem that
has been intensely studied, different instances are best solved using different
algorithms. This is particularly pronounced for computationally hard problems,
where in most cases, no single algorithm defines the state of the art; instead,
there is a set of algorithms with complementary strengths. This performance complementarity
can be exploited in various ways, one of which is based on the idea of selecting,
from a set of given algorithms, for each problem instance to be solved the one
expected to perform best. The task of automatically selecting an algorithm from
a given set is known as the per-instance algorithm selection problem and has been
intensely studied over the past 15 years, leading to major improvements in the
state of the art in solving a growing number of discrete combinatorial problems,
including propositional satisfiability and AI planning. Per-instance algorithm
selection also shows much promise for boosting performance in solving continuous
and mixed discrete/continuous optimisation problems. This survey provides an overview
of research in automated algorithm selection, ranging from early and seminal works
to recent and promising application areas. Different from earlier work, it covers
applications to discrete and continuous problems, and discusses algorithm selection
in context with conceptually related approaches, such as algorithm configuration,
scheduling, or portfolio selection. Since informative and cheaply computable problem
instance features provide the basis for effective per-instance algorithm selection
systems, we also provide an overview of such features for discrete and continuous
problems. Finally, we provide perspectives on future work in the area and discuss
a number of open research challenges.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Holger H
full_name: Hoos, Holger H
last_name: Hoos
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Hoos HH, Neumann F, Trautmann H. Automated Algorithm Selection:
Survey and Perspectives. Evolutionary Computation (ECJ). 2019;27(1):3–45.
doi:10.1162/evco_a_00242'
apa: 'Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated
Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ),
27(1), 3–45. https://doi.org/10.1162/evco_a_00242'
bibtex: '@article{Kerschke_Hoos_Neumann_Trautmann_2019, title={Automated Algorithm
Selection: Survey and Perspectives}, volume={27}, DOI={10.1162/evco_a_00242},
number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal
and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={3–45}
}'
chicago: 'Kerschke, Pascal, Holger H Hoos, Frank Neumann, and Heike Trautmann. “Automated
Algorithm Selection: Survey and Perspectives.” Evolutionary Computation (ECJ)
27, no. 1 (2019): 3–45. https://doi.org/10.1162/evco_a_00242.'
ieee: 'P. Kerschke, H. H. Hoos, F. Neumann, and H. Trautmann, “Automated Algorithm
Selection: Survey and Perspectives,” Evolutionary Computation (ECJ), vol.
27, no. 1, pp. 3–45, 2019, doi: 10.1162/evco_a_00242.'
mla: 'Kerschke, Pascal, et al. “Automated Algorithm Selection: Survey and Perspectives.”
Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 3–45, doi:10.1162/evco_a_00242.'
short: P. Kerschke, H.H. Hoos, F. Neumann, H. Trautmann, Evolutionary Computation
(ECJ) 27 (2019) 3–45.
date_created: 2023-08-04T07:50:33Z
date_updated: 2023-10-16T13:31:40Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco_a_00242
intvolume: ' 27'
issue: '1'
language:
- iso: eng
page: 3–45
publication: Evolutionary Computation (ECJ)
status: public
title: 'Automated Algorithm Selection: Survey and Perspectives'
type: journal_article
user_id: '15504'
volume: 27
year: '2019'
...
---
_id: '46344'
abstract:
- lang: eng
text: Analyzing data streams has received considerable attention over the past decades
due to the widespread usage of sensors, social media and other streaming data
sources. A core research area in this field is stream clustering which aims to
recognize patterns in an unordered, infinite and evolving stream of observations.
Clustering can be a crucial support in decision making, since it aims for an optimized
aggregated representation of a continuous data stream over time and allows to
identify patterns in large and high-dimensional data. A multitude of algorithms
and approaches has been developed that are able to find and maintain clusters
over time in the challenging streaming scenario. This survey explores, summarizes
and categorizes a total of 51 stream clustering algorithms and identifies core
research threads over the past decades. In particular, it identifies categories
of algorithms based on distance thresholds, density grids and statistical models
as well as algorithms for high dimensional data. Furthermore, it discusses applications
scenarios, available software and how to configure stream clustering algorithms.
This survey is considerably more extensive than comparable studies, more up-to-date
and highlights how concepts are interrelated and have been developed over time.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Carnein M, Trautmann H. Optimizing Data Stream Representation: An Extensive
Survey on Stream Clustering Algorithms. Business and Information Systems Engineering
(BISE). 2019;61(3):277–297.'
apa: 'Carnein, M., & Trautmann, H. (2019). Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms. Business and Information
Systems Engineering (BISE), 61(3), 277–297.'
bibtex: '@article{Carnein_Trautmann_2019, title={Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms}, volume={61}, number={3},
journal={Business and Information Systems Engineering (BISE)}, author={Carnein,
Matthias and Trautmann, Heike}, year={2019}, pages={277–297} }'
chicago: 'Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms.” Business and Information
Systems Engineering (BISE) 61, no. 3 (2019): 277–297.'
ieee: 'M. Carnein and H. Trautmann, “Optimizing Data Stream Representation: An Extensive
Survey on Stream Clustering Algorithms,” Business and Information Systems Engineering
(BISE), vol. 61, no. 3, pp. 277–297, 2019.'
mla: 'Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms.” Business and Information
Systems Engineering (BISE), vol. 61, no. 3, 2019, pp. 277–297.'
short: M. Carnein, H. Trautmann, Business and Information Systems Engineering (BISE)
61 (2019) 277–297.
date_created: 2023-08-04T07:49:47Z
date_updated: 2023-10-16T13:31:21Z
department:
- _id: '34'
- _id: '819'
intvolume: ' 61'
issue: '3'
language:
- iso: eng
page: 277–297
publication: Business and Information Systems Engineering (BISE)
status: public
title: 'Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering
Algorithms'
type: journal_article
user_id: '15504'
volume: 61
year: '2019'
...
---
_id: '46340'
abstract:
- lang: eng
text: Recommender systems aim to provide personalized suggestions to customers which
products to buy or services to consume. They can help to increase sales by helping
customers discover new and relevant products. Traditionally, recommender systems
use the purchase history of a customer, e.g., the purchased quantity or properties
of the items. While this allows to build personalized recommendations, it is a
very limited view of the problem. Nowadays, extensive information about customers
and their personal preferences is available which goes far beyond their purchase
behaviour. For example, customers reveal their preferences in social media, by
their browsing habits and online search behaviour or their interest in specific
newsletters. In this paper, we investigate how information from different sources
and channels can be collected and incorporated into the recommendation process.
We demonstrate this, based on a real-life case study of a retailer with several
million transactions. We discuss how to employ a recommender system in this scenario,
evaluate various recommendation strategies and describe how to incorporate information
from different sources and channels, both internal and external. Our results show
that the recommendations can be better tailored to the personal preferences of
customers.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Leschek
full_name: Homann, Leschek
last_name: Homann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Gottfried
full_name: Vossen, Gottfried
last_name: Vossen
citation:
ama: 'Carnein M, Homann L, Trautmann H, Vossen G. A Recommender System Based on
Omni-Channel Customer Data. In: Proceedings of the 21$^st$ IEEE Conference
on Business Informatics (CBI’ 19). ; 2019:65–74.'
apa: Carnein, M., Homann, L., Trautmann, H., & Vossen, G. (2019). A Recommender
System Based on Omni-Channel Customer Data. Proceedings of the 21$^st$ IEEE
Conference on Business Informatics (CBI’ 19), 65–74.
bibtex: '@inproceedings{Carnein_Homann_Trautmann_Vossen_2019, place={Moscow, Russia},
title={A Recommender System Based on Omni-Channel Customer Data}, booktitle={Proceedings
of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}, author={Carnein,
Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}, year={2019},
pages={65–74} }'
chicago: Carnein, Matthias, Leschek Homann, Heike Trautmann, and Gottfried Vossen.
“A Recommender System Based on Omni-Channel Customer Data.” In Proceedings
of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 65–74. Moscow,
Russia, 2019.
ieee: M. Carnein, L. Homann, H. Trautmann, and G. Vossen, “A Recommender System
Based on Omni-Channel Customer Data,” in Proceedings of the 21$^st$ IEEE Conference
on Business Informatics (CBI’ 19), 2019, pp. 65–74.
mla: Carnein, Matthias, et al. “A Recommender System Based on Omni-Channel Customer
Data.” Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’
19), 2019, pp. 65–74.
short: 'M. Carnein, L. Homann, H. Trautmann, G. Vossen, in: Proceedings of the 21$^st$
IEEE Conference on Business Informatics (CBI’ 19), Moscow, Russia, 2019, pp. 65–74.'
date_created: 2023-08-04T07:46:20Z
date_updated: 2023-10-16T13:29:53Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 65–74
place: Moscow, Russia
publication: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’
19)
status: public
title: A Recommender System Based on Omni-Channel Customer Data
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46341'
abstract:
- lang: eng
text: Customer Segmentation aims to identify groups of customers that share similar
interest or behaviour. It is an essential tool in marketing and can be used to
target customer segments with tailored marketing strategies. Customer segmentation
is often based on clustering techniques. This analysis is typically performed
as a snapshot analysis where segments are identified at a specific point in time.
However, this ignores the fact that customer segments are highly volatile and
segments change over time. Once segments change, the entire analysis needs to
be repeated and strategies adapted. In this paper we explore stream clustering
as a tool to alleviate this problem. We propose a new stream clustering algorithm
which allows to identify and track customer segments over time. The biggest challenge
is that customer segmentation often relies on the transaction history of a customer.
Since this data changes over time, it is necessary to update customers which have
already been incorporated into the clustering. We show how to perform this step
incrementally, without the need for periodic re-computations. As a result, customer
segmentation can be performed continuously, faster and is more scalable. We demonstrate
the performance of our algorithm using a large real-life case study.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Carnein M, Trautmann H. Customer Segmentation Based on Transactional Data
Using Stream Clustering. In: Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19). ; 2019:280–292.'
apa: Carnein, M., & Trautmann, H. (2019). Customer Segmentation Based on Transactional
Data Using Stream Clustering. Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), 280–292.
bibtex: '@inproceedings{Carnein_Trautmann_2019, place={Macau, China}, title={Customer
Segmentation Based on Transactional Data Using Stream Clustering}, booktitle={Proceedings
of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={280–292}
}'
chicago: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on
Transactional Data Using Stream Clustering.” In Proceedings of the 23$^rd$
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19),
280–292. Macau, China, 2019.
ieee: M. Carnein and H. Trautmann, “Customer Segmentation Based on Transactional
Data Using Stream Clustering,” in Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), 2019, pp. 280–292.
mla: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional
Data Using Stream Clustering.” Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), 2019, pp. 280–292.
short: 'M. Carnein, H. Trautmann, in: Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), Macau, China, 2019, pp. 280–292.'
date_created: 2023-08-04T07:47:20Z
date_updated: 2023-10-16T13:30:10Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 280–292
place: Macau, China
publication: Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery
and Data Mining (PAKDD ’19)
status: public
title: Customer Segmentation Based on Transactional Data Using Stream Clustering
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46342'
abstract:
- lang: eng
text: There is a range of phenomena in continuous, global multi-objective optimization,
that cannot occur in single-objective optimization. For instance, in some multi-objective
optimization problems it is possible to follow continuous paths of gradients of
straightforward weighted scalarization functions, starting from locally efficient
solutions, in order to reach globally Pareto optimal solutions. This paper seeks
to better characterize multimodal multi-objective landscapes and to better understand
the transitions from local optima to global optima in simple, path-oriented search
procedures.
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Michael T M
full_name: Emmerich, Michael T M
last_name: Emmerich
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: André H
full_name: Deutz, André H
last_name: Deutz
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Grimme C, Kerschke P, Emmerich MTM, Preuss M, Deutz AH, Trautmann H. Sliding
to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective
Optimization. In: AIP Conference Proceedings. AIP Publishing; 2019:020052-1-020052-020054.
doi:10.1063/1.5090019'
apa: 'Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., &
Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global
Optima in Multimodal Multi-Objective Optimization. AIP Conference Proceedings,
020052-1-020052–020054. https://doi.org/10.1063/1.5090019'
bibtex: '@inproceedings{Grimme_Kerschke_Emmerich_Preuss_Deutz_Trautmann_2019, place={Leiden,
The Netherlands}, title={Sliding to the Global Optimum: How to Benefit from Non-Global
Optima in Multimodal Multi-Objective Optimization}, DOI={10.1063/1.5090019},
booktitle={AIP Conference Proceedings}, publisher={AIP Publishing}, author={Grimme,
Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and
Deutz, André H and Trautmann, Heike}, year={2019}, pages={020052-1-020052–4} }'
chicago: 'Grimme, Christian, Pascal Kerschke, Michael T M Emmerich, Mike Preuss,
André H Deutz, and Heike Trautmann. “Sliding to the Global Optimum: How to Benefit
from Non-Global Optima in Multimodal Multi-Objective Optimization.” In AIP
Conference Proceedings, 020052-1-020052–54. Leiden, The Netherlands: AIP Publishing,
2019. https://doi.org/10.1063/1.5090019.'
ieee: 'C. Grimme, P. Kerschke, M. T. M. Emmerich, M. Preuss, A. H. Deutz, and H.
Trautmann, “Sliding to the Global Optimum: How to Benefit from Non-Global Optima
in Multimodal Multi-Objective Optimization,” in AIP Conference Proceedings,
2019, pp. 020052-1-020052–4, doi: 10.1063/1.5090019.'
mla: 'Grimme, Christian, et al. “Sliding to the Global Optimum: How to Benefit from
Non-Global Optima in Multimodal Multi-Objective Optimization.” AIP Conference
Proceedings, AIP Publishing, 2019, pp. 020052-1-020052–54, doi:10.1063/1.5090019.'
short: 'C. Grimme, P. Kerschke, M.T.M. Emmerich, M. Preuss, A.H. Deutz, H. Trautmann,
in: AIP Conference Proceedings, AIP Publishing, Leiden, The Netherlands, 2019,
pp. 020052-1-020052–4.'
date_created: 2023-08-04T07:48:15Z
date_updated: 2023-10-16T13:30:43Z
department:
- _id: '34'
- _id: '819'
doi: 10.1063/1.5090019
language:
- iso: eng
page: 020052-1-020052-4
place: Leiden, The Netherlands
publication: AIP Conference Proceedings
publisher: AIP Publishing
status: public
title: 'Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal
Multi-Objective Optimization'
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46336'
abstract:
- lang: eng
text: Choosing the best-performing optimizer(s) out of a portfolio of optimization
algorithms is usually a difficult and complex task. It gets even worse, if the
underlying functions are unknown, i.e., so-called black-box problems, and function
evaluations are considered to be expensive. In case of continuous single-objective
optimization problems, exploratory landscape analysis (ELA), a sophisticated and
effective approach for characterizing the landscapes of such problems by means
of numerical values before actually performing the optimization task itself, is
advantageous. Unfortunately, until now it has been quite complicated to compute
multiple ELA features simultaneously, as the corresponding code has been—if at
all—spread across multiple platforms or at least across several packages within
these platforms. This article presents a broad summary of existing ELA approaches
and introduces flacco, an R-package for feature-based landscape analysis of continuous
and constrained optimization problems. Although its functions neither solve the
optimization problem itself nor the related algorithm selection problem (ASP),
it offers easy access to an essential ingredient of the ASP by providing a wide
collection of ELA features on a single platform—even within a single package.
In addition, flacco provides multiple visualization techniques, which enhance
the understanding of some of these numerical features, and thereby make certain
landscape properties more comprehensible. On top of that, we will introduce the
package’s built-in, as well as web-hosted and hence platform-independent, graphical
user interface (GUI). It facilitates the usage of the package—especially for people
who are not familiar with R—and thus makes flacco a very convenient toolbox when
working towards algorithm selection of continuous single-objective optimization
problems.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Trautmann H. Comprehensive Feature-Based Landscape Analysis of
Continuous and Constrained Optimization Problems Using the R-package flacco. In:
Bauer N, Ickstadt K, Lübke K, Szepannek G, Trautmann H, Vichi M, eds. Applications
in Statistical Computing. Springer; 2019:93–123. doi:10.1007/978-3-030-25147-5_7'
apa: Kerschke, P., & Trautmann, H. (2019). Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-package
flacco. In N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, &
M. Vichi (Eds.), Applications in Statistical Computing (pp. 93–123). Springer.
https://doi.org/10.1007/978-3-030-25147-5_7
bibtex: '@inbook{Kerschke_Trautmann_2019, title={Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-package
flacco}, DOI={10.1007/978-3-030-25147-5_7},
booktitle={Applications in Statistical Computing}, publisher={Springer}, author={Kerschke,
Pascal and Trautmann, Heike}, editor={Bauer, Nadja and Ickstadt, Katja and Lübke,
Karsten and Szepannek, Gero and Trautmann, Heike and Vichi, Maurizio}, year={2019},
pages={93–123} }'
chicago: Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-Package
Flacco.” In Applications in Statistical Computing, edited by Nadja Bauer,
Katja Ickstadt, Karsten Lübke, Gero Szepannek, Heike Trautmann, and Maurizio Vichi,
93–123. Springer, 2019. https://doi.org/10.1007/978-3-030-25147-5_7.
ieee: P. Kerschke and H. Trautmann, “Comprehensive Feature-Based Landscape Analysis
of Continuous and Constrained Optimization Problems Using the R-package flacco,”
in Applications in Statistical Computing, N. Bauer, K. Ickstadt, K. Lübke,
G. Szepannek, H. Trautmann, and M. Vichi, Eds. Springer, 2019, pp. 93–123.
mla: Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-Package
Flacco.” Applications in Statistical Computing, edited by Nadja Bauer et
al., Springer, 2019, pp. 93–123, doi:10.1007/978-3-030-25147-5_7.
short: 'P. Kerschke, H. Trautmann, in: N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek,
H. Trautmann, M. Vichi (Eds.), Applications in Statistical Computing, Springer,
2019, pp. 93–123.'
date_created: 2023-08-04T07:43:30Z
date_updated: 2023-10-16T13:08:22Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-25147-5_7
editor:
- first_name: Nadja
full_name: Bauer, Nadja
last_name: Bauer
- first_name: Katja
full_name: Ickstadt, Katja
last_name: Ickstadt
- first_name: Karsten
full_name: Lübke, Karsten
last_name: Lübke
- first_name: Gero
full_name: Szepannek, Gero
last_name: Szepannek
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Maurizio
full_name: Vichi, Maurizio
last_name: Vichi
language:
- iso: eng
page: 93–123
publication: Applications in Statistical Computing
publisher: Springer
status: public
title: Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained
Optimization Problems Using the R-package flacco
type: book_chapter
user_id: '15504'
year: '2019'
...
---
_id: '46335'
author:
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Trautmann H. Applications in Statistical Computing — From Music Data Analysis
to Industrial Quality Improvement. Springer International Publishing; 2019.
apa: Trautmann, H. (2019). Applications in Statistical Computing — From Music
Data Analysis to Industrial Quality Improvement. Springer International Publishing.
bibtex: '@book{Trautmann_2019, series={Studies in Classification, Data Analysis,
and Knowledge Organization}, title={Applications in Statistical Computing — From
Music Data Analysis to Industrial Quality Improvement}, publisher={Springer International
Publishing}, author={Trautmann, Heike}, year={2019}, collection={Studies in Classification,
Data Analysis, and Knowledge Organization} }'
chicago: Trautmann, Heike. Applications in Statistical Computing — From Music
Data Analysis to Industrial Quality Improvement. Studies in Classification,
Data Analysis, and Knowledge Organization. Springer International Publishing,
2019.
ieee: H. Trautmann, Applications in Statistical Computing — From Music Data Analysis
to Industrial Quality Improvement. Springer International Publishing, 2019.
mla: Trautmann, Heike. Applications in Statistical Computing — From Music Data
Analysis to Industrial Quality Improvement. Springer International Publishing,
2019.
short: H. Trautmann, Applications in Statistical Computing — From Music Data Analysis
to Industrial Quality Improvement, Springer International Publishing, 2019.
date_created: 2023-08-04T07:43:09Z
date_updated: 2023-10-16T13:07:21Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-030-25147-5
publisher: Springer International Publishing
series_title: Studies in Classification, Data Analysis, and Knowledge Organization
status: public
title: Applications in Statistical Computing — From Music Data Analysis to Industrial
Quality Improvement
type: book
user_id: '15504'
year: '2019'
...
---
_id: '46346'
abstract:
- lang: eng
text: In this article, we build upon previous work on designing informative and
efficient Exploratory Landscape Analysis features for characterizing problems'
landscapes and show their effectiveness in automatically constructing algorithm
selection models in continuous black-box optimization problems. Focusing on algorithm
performance results of the COCO platform of several years, we construct a representative
set of high-performing complementary solvers and present an algorithm selection
model that, compared to the portfolio's single best solver, on average requires
less than half of the resources for solving a given problem. Therefore, there
is a huge gain in efficiency compared to classical ensemble methods combined with
an increased insight into problem characteristics and algorithm properties by
using informative features. The model acts on the assumption that the function
set of the Black-Box Optimization Benchmark is representative enough for practical
applications. The model allows for selecting the best suited optimization algorithm
within the considered set for unseen problems prior to the optimization itself
based on a small sample of function evaluations. Note that such a sample can even
be reused for the initial population of an evolutionary (optimization) algorithm
so that even the feature costs become negligible.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Kerschke P, Trautmann H. Automated Algorithm Selection on Continuous Black-Box
Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary
Computation (ECJ). 2019;27(1):99–127. doi:10.1162/evco_a_00236
apa: Kerschke, P., & Trautmann, H. (2019). Automated Algorithm Selection on
Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and
Machine Learning. Evolutionary Computation (ECJ), 27(1), 99–127.
https://doi.org/10.1162/evco_a_00236
bibtex: '@article{Kerschke_Trautmann_2019, title={Automated Algorithm Selection
on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and
Machine Learning}, volume={27}, DOI={10.1162/evco_a_00236},
number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal
and Trautmann, Heike}, year={2019}, pages={99–127} }'
chicago: 'Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection
on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and
Machine Learning.” Evolutionary Computation (ECJ) 27, no. 1 (2019): 99–127.
https://doi.org/10.1162/evco_a_00236.'
ieee: 'P. Kerschke and H. Trautmann, “Automated Algorithm Selection on Continuous
Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning,”
Evolutionary Computation (ECJ), vol. 27, no. 1, pp. 99–127, 2019, doi:
10.1162/evco_a_00236.'
mla: Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous
Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.”
Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 99–127, doi:10.1162/evco_a_00236.
short: P. Kerschke, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 99–127.
date_created: 2023-08-04T07:51:18Z
date_updated: 2023-10-16T13:31:57Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco_a_00236
intvolume: ' 27'
issue: '1'
language:
- iso: eng
page: 99–127
publication: Evolutionary Computation (ECJ)
status: public
title: Automated Algorithm Selection on Continuous Black-Box Problems By Combining
Exploratory Landscape Analysis and Machine Learning
type: journal_article
user_id: '15504'
volume: 27
year: '2019'
...
---
_id: '46347'
abstract:
- lang: eng
text: We continue recent work on the definition of multimodality in multiobjective
optimization (MO) and the introduction of a test bed for multimodal MO problems.
This goes beyond well-known diversity maintenance approaches but instead focuses
on the landscape topology induced by the objective functions. More general multimodal
MO problems are considered by allowing ellipsoid contours for single-objective
subproblems. An experimental analysis compares two MO algorithms, one that explicitly
relies on hypervolume gradient approximation, and one that is based on local search,
both on a selection of generated example problems. We do not focus on performance
but on the interaction induced by the problems and algorithms, which can be described
by means of specific characteristics explicitly designed for the multimodal MO
setting. Furthermore, we widen the scope of our analysis by additionally applying
visualization techniques in the decision space. This strengthens and extends the
foundations for Exploratory Landscape Analysis (ELA) in MO.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: André
full_name: Deutz, André
last_name: Deutz
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Michael
full_name: Emmerich, Michael
last_name: Emmerich
citation:
ama: Kerschke P, Wang H, Preuss M, et al. Search Dynamics on Multimodal Multi-Objective
Problems. Evolutionary Computation (ECJ). 2019;27(4):577–609. doi:10.1162/evco_a_00234
apa: Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., &
Emmerich, M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary
Computation (ECJ), 27(4), 577–609. https://doi.org/10.1162/evco_a_00234
bibtex: '@article{Kerschke_Wang_Preuss_Grimme_Deutz_Trautmann_Emmerich_2019, title={Search
Dynamics on Multimodal Multi-Objective Problems}, volume={27}, DOI={10.1162/evco_a_00234},
number={4}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal
and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann,
Heike and Emmerich, Michael}, year={2019}, pages={577–609} }'
chicago: 'Kerschke, Pascal, Hao Wang, Mike Preuss, Christian Grimme, André Deutz,
Heike Trautmann, and Michael Emmerich. “Search Dynamics on Multimodal Multi-Objective
Problems.” Evolutionary Computation (ECJ) 27, no. 4 (2019): 577–609. https://doi.org/10.1162/evco_a_00234.'
ieee: 'P. Kerschke et al., “Search Dynamics on Multimodal Multi-Objective
Problems,” Evolutionary Computation (ECJ), vol. 27, no. 4, pp. 577–609,
2019, doi: 10.1162/evco_a_00234.'
mla: Kerschke, Pascal, et al. “Search Dynamics on Multimodal Multi-Objective Problems.”
Evolutionary Computation (ECJ), vol. 27, no. 4, 2019, pp. 577–609, doi:10.1162/evco_a_00234.
short: P. Kerschke, H. Wang, M. Preuss, C. Grimme, A. Deutz, H. Trautmann, M. Emmerich,
Evolutionary Computation (ECJ) 27 (2019) 577–609.
date_created: 2023-08-04T07:52:06Z
date_updated: 2023-10-16T13:32:18Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco_a_00234
intvolume: ' 27'
issue: '4'
language:
- iso: eng
page: 577–609
publication: Evolutionary Computation (ECJ)
status: public
title: Search Dynamics on Multimodal Multi-Objective Problems
type: journal_article
user_id: '15504'
volume: 27
year: '2019'
...
---
_id: '48841'
abstract:
- lang: eng
text: We tackle a bi-objective dynamic orienteering problem where customer requests
arise as time passes by. The goal is to minimize the tour length traveled by a
single delivery vehicle while simultaneously keeping the number of dismissed dynamic
customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm
which is grounded on insights gained from a previous series of work on an a-posteriori
version of the problem, where all request times are known in advance. In our experiments,
we simulate different decision maker strategies and evaluate the development of
the Pareto-front approximations on exemplary problem instances. It turns out,
that despite severely reduced computational budget and no oracle-knowledge of
request times the dynamic EMOA is capable of producing approximations which partially
dominate the results of the a-posteriori EMOA and dynamic integer linear programming
strategies.
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: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering:
Towards a Dynamic Multi-objective Evolutionary Algorithm. In: Deb K, Goodman E,
Coello Coello CA, et al., eds. Evolutionary Multi-Criterion Optimization (EMO).
Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528.
doi:10.1007/978-3-030-12598-1_41'
apa: 'Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019).
Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm.
In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim,
& P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp.
516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41'
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={Cham},
series={Lecture Notes in Computer Science}, title={Bi-Objective Orienteering:
Towards a Dynamic Multi-objective Evolutionary Algorithm}, DOI={10.1007/978-3-030-12598-1_41},
booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer
International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel,
Stephan and Rudolph, Günter and Trautmann, Heike}, editor={Deb, Kalyanmoy and
Goodman, Erik and Coello Coello, Carlos A. and Klamroth, Kathrin and Miettinen,
Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={516–528}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike
Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary
Algorithm.” In Evolutionary Multi-Criterion Optimization (EMO), edited
by Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa
Miettinen, Sanaz Mostaghim, and Patrick Reed, 516–528. Lecture Notes in Computer
Science. Cham: Springer International Publishing, 2019. https://doi.org/10.1007/978-3-030-12598-1_41.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective
Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm,” in Evolutionary
Multi-Criterion Optimization (EMO), 2019, pp. 516–528, doi: 10.1007/978-3-030-12598-1_41.'
mla: 'Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective
Evolutionary Algorithm.” Evolutionary Multi-Criterion Optimization (EMO),
edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528,
doi:10.1007/978-3-030-12598-1_41.'
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E.
Goodman, C.A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed
(Eds.), Evolutionary Multi-Criterion Optimization (EMO), Springer International
Publishing, Cham, 2019, pp. 516–528.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:07Z
department:
- _id: '819'
doi: 10.1007/978-3-030-12598-1_41
editor:
- first_name: Kalyanmoy
full_name: Deb, Kalyanmoy
last_name: Deb
- first_name: Erik
full_name: Goodman, Erik
last_name: Goodman
- first_name: Carlos A.
full_name: Coello Coello, Carlos A.
last_name: Coello Coello
- first_name: Kathrin
full_name: Klamroth, Kathrin
last_name: Klamroth
- first_name: Kaisa
full_name: Miettinen, Kaisa
last_name: Miettinen
- first_name: Sanaz
full_name: Mostaghim, Sanaz
last_name: Mostaghim
- first_name: Patrick
full_name: Reed, Patrick
last_name: Reed
extern: '1'
keyword:
- Combinatorial optimization
- Dynamic optimization
- Metaheuristics
- Multi-objective optimization
- Vehicle routing
language:
- iso: eng
page: 516–528
place: Cham
publication: Evolutionary Multi-Criterion Optimization (EMO)
publication_identifier:
isbn:
- 978-3-030-12598-1
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: 'Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary
Algorithm'
type: conference
user_id: '102979'
year: '2019'
...
---
_id: '48842'
abstract:
- lang: eng
text: 'Evolutionary algorithms have successfully been applied to evolve problem
instances that exhibit a significant difference in performance for a given algorithm
or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP).
Creating a large variety of instances is crucial for successful applications in
the blooming field of algorithm selection. In this paper, we introduce new and
creative mutation operators for evolving instances of the TSP. We show that adopting
those operators in an evolutionary algorithm allows for the generation of benchmark
sets with highly desirable properties: (1) novelty by clear visual distinction
to established benchmark sets in the field, (2) visual and quantitative diversity
in the space of TSP problem characteristics, and (3) significant performance differences
with respect to the restart versions of heuristic state-of-the-art TSP solvers
EAX and LKH. The important aspect of diversity is addressed and achieved solely
by the proposed mutation operators and not enforced by explicit diversity preservation.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving
Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: Proceedings
of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA
’19. Association for Computing Machinery; 2019:58–71. doi:10.1145/3299904.3340307'
apa: Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann,
H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation
Operators. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of
Genetic Algorithms, 58–71. https://doi.org/10.1145/3299904.3340307
bibtex: '@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={New
York, NY, USA}, series={FOGA ’19}, title={Evolving Diverse TSP Instances by Means
of Novel and Creative Mutation Operators}, DOI={10.1145/3299904.3340307},
booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms}, publisher={Association for Computing Machinery}, author={Bossek,
Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann,
Frank and Trautmann, Heike}, year={2019}, pages={58–71}, collection={FOGA ’19}
}'
chicago: 'Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann,
and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative
Mutation Operators.” In Proceedings of the 15th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, 58–71. FOGA ’19. New York, NY, USA: Association for
Computing Machinery, 2019. https://doi.org/10.1145/3299904.3340307.'
ieee: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann,
“Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,”
in Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms, 2019, pp. 58–71, doi: 10.1145/3299904.3340307.'
mla: Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and
Creative Mutation Operators.” Proceedings of the 15th ACM/SIGEVO Conference
on Foundations of Genetic Algorithms, Association for Computing Machinery,
2019, pp. 58–71, doi:10.1145/3299904.3340307.
short: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann,
in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms,
Association for Computing Machinery, New York, NY, USA, 2019, pp. 58–71.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:42:57Z
department:
- _id: '819'
doi: 10.1145/3299904.3340307
extern: '1'
keyword:
- benchmarking
- instance features
- optimization
- problem generation
- traveling salesperson problem
language:
- iso: eng
page: 58–71
place: New York, NY, USA
publication: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- 978-1-4503-6254-2
publication_status: published
publisher: Association for Computing Machinery
series_title: FOGA ’19
status: public
title: Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators
type: conference
user_id: '102979'
year: '2019'
...
---
_id: '48843'
abstract:
- lang: eng
text: We contribute to the theoretical understanding of randomized search heuristics
for dynamic problems. We consider the classical graph coloring problem and investigate
the dynamic setting where edges are added to the current graph. We then analyze
the expected time for randomized search heuristics to recompute high quality solutions.
This includes the (1+1) EA and RLS in a setting where the number of colors is
bounded and we are minimizing the number of conflicts as well as iterated local
search algorithms that use an unbounded color palette and aim to use the smallest
colors and - as a consequence - the smallest number of colors. We identify classes
of bipartite graphs where reoptimization is as hard as or even harder than optimization
from scratch, i. e. starting with a random initialization. Even adding a single
edge can lead to hard symmetry problems. However, graph classes that are hard
for one algorithm turn out to be easy for others. In most cases our bounds show
that reoptimization is faster than optimizing from scratch. Furthermore, we show
how to speed up computations by using problem specific operators concentrating
on parts of the graph where changes have occurred.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Neumann F, Peng P, Sudholt D. Runtime Analysis of Randomized Search
Heuristics for Dynamic Graph Coloring. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO ’19. Association for Computing Machinery; 2019:1443–1451.
doi:10.1145/3321707.3321792'
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime Analysis
of Randomized Search Heuristics for Dynamic Graph Coloring. Proceedings of
the Genetic and Evolutionary Computation Conference, 1443–1451. https://doi.org/10.1145/3321707.3321792
bibtex: '@inproceedings{Bossek_Neumann_Peng_Sudholt_2019, place={New York, NY, USA},
series={GECCO ’19}, title={Runtime Analysis of Randomized Search Heuristics for
Dynamic Graph Coloring}, DOI={10.1145/3321707.3321792},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank and Peng, Pan and Sudholt, Dirk}, year={2019}, pages={1443–1451}, collection={GECCO
’19} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Runtime Analysis
of Randomized Search Heuristics for Dynamic Graph Coloring.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 1443–1451. GECCO ’19.
New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3321707.3321792.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Runtime Analysis of Randomized
Search Heuristics for Dynamic Graph Coloring,” in Proceedings of the Genetic
and Evolutionary Computation Conference, 2019, pp. 1443–1451, doi: 10.1145/3321707.3321792.'
mla: Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for
Dynamic Graph Coloring.” Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, 2019, pp. 1443–1451, doi:10.1145/3321707.3321792.
short: 'J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2019, pp. 1443–1451.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:42:37Z
department:
- _id: '819'
doi: 10.1145/3321707.3321792
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1443–1451
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: Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring
type: conference
user_id: '102979'
year: '2019'
...
---
_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: '48858'
abstract:
- lang: eng
text: The $$\textbackslash mathcal NP$$-hard multi-criteria shortest path problem
(mcSPP) is of utmost practical relevance, e.~g., in navigation system design and
logistics. We address the problem of approximating the Pareto-front of the mcSPP
with sum objectives. We do so by proposing a new mutation operator for multi-objective
evolutionary algorithms that solves single-objective versions of the shortest
path problem on subgraphs. A rigorous empirical benchmark on a diverse set of
problem instances shows the effectiveness of the approach in comparison to a well-known
mutation operator in terms of convergence speed and approximation quality. In
addition, we glance at the neighbourhood structure and similarity of obtained
Pareto-optimal solutions and derive promising directions for future work.
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
citation:
ama: 'Bossek J, Grimme C. Solving Scalarized Subproblems within Evolutionary Algorithms
for Multi-criteria Shortest Path Problems. In: Battiti R, Brunato M, Kotsireas
I, Pardalos PM, eds. Learning and Intelligent Optimization. Lecture Notes
in Computer Science. Springer International Publishing; 2019:184–198. doi:10.1007/978-3-030-05348-2_17'
apa: Bossek, J., & Grimme, C. (2019). Solving Scalarized Subproblems within
Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti,
M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent
Optimization (pp. 184–198). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_17
bibtex: '@inproceedings{Bossek_Grimme_2019, place={Cham}, series={Lecture Notes
in Computer Science}, title={Solving Scalarized Subproblems within Evolutionary
Algorithms for Multi-criteria Shortest Path Problems}, DOI={10.1007/978-3-030-05348-2_17},
booktitle={Learning and Intelligent Optimization}, publisher={Springer International
Publishing}, author={Bossek, Jakob and Grimme, Christian}, editor={Battiti, Roberto
and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019},
pages={184–198}, collection={Lecture Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within
Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” In Learning
and Intelligent Optimization, edited by Roberto Battiti, Mauro Brunato, Ilias
Kotsireas, and Panos M. Pardalos, 184–198. Lecture Notes in Computer Science.
Cham: Springer International Publishing, 2019. https://doi.org/10.1007/978-3-030-05348-2_17.'
ieee: 'J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary
Algorithms for Multi-criteria Shortest Path Problems,” in Learning and Intelligent
Optimization, 2019, pp. 184–198, doi: 10.1007/978-3-030-05348-2_17.'
mla: Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within
Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” Learning
and Intelligent Optimization, edited by Roberto Battiti et al., Springer International
Publishing, 2019, pp. 184–198, doi:10.1007/978-3-030-05348-2_17.
short: 'J. Bossek, C. Grimme, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos
(Eds.), Learning and Intelligent Optimization, Springer International Publishing,
Cham, 2019, pp. 184–198.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:44Z
department:
- _id: '819'
doi: 10.1007/978-3-030-05348-2_17
editor:
- first_name: Roberto
full_name: Battiti, Roberto
last_name: Battiti
- first_name: Mauro
full_name: Brunato, Mauro
last_name: Brunato
- first_name: Ilias
full_name: Kotsireas, Ilias
last_name: Kotsireas
- first_name: Panos M.
full_name: Pardalos, Panos M.
last_name: Pardalos
extern: '1'
language:
- iso: eng
page: 184–198
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-030-05348-2
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria
Shortest Path Problems
type: conference
user_id: '102979'
year: '2019'
...
---
_id: '48870'
abstract:
- lang: eng
text: The edge coloring problem asks for an assignment of colors to edges of a graph
such that no two incident edges share the same color and the number of colors
is minimized. It is known that all graphs with maximum degree {$\Delta$} can be
colored with {$\Delta$} or {$\Delta$} + 1 colors, but it is NP-hard to determine
whether {$\Delta$} colors are sufficient. We present the first runtime analysis
of evolutionary algorithms (EAs) for the edge coloring problem. Simple EAs such
as RLS and (1+1) EA efficiently find (2{$\Delta$} - 1)-colorings on arbitrary
graphs and optimal colorings for even and odd cycles, paths, star graphs and arbitrary
trees. A partial analysis for toroids also suggests efficient runtimes in bipartite
graphs with many cycles. Experiments support these findings and investigate additional
graph classes such as hypercubes, complete graphs and complete bipartite graphs.
Theoretical and experimental results suggest that simple EAs find optimal colorings
for all these graph classes in expected time O({$\Delta\mathscrl$}2m log m), where
m is the number of edges and {$\mathscrl$} is the length of the longest simple
path in the graph.
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. Time Complexity Analysis of RLS and (1 + 1) EA for the
Edge Coloring Problem. In: Proceedings of the 15th ACM/SIGEVO Conference on
Foundations of Genetic Algorithms. FOGA ’19. Association for Computing Machinery;
2019:102–115. doi:10.1145/3299904.3340311'
apa: Bossek, J., & Sudholt, D. (2019). Time Complexity Analysis of RLS and (1
+ 1) EA for the Edge Coloring Problem. Proceedings of the 15th ACM/SIGEVO Conference
on Foundations of Genetic Algorithms, 102–115. https://doi.org/10.1145/3299904.3340311
bibtex: '@inproceedings{Bossek_Sudholt_2019, place={New York, NY, USA}, series={FOGA
’19}, title={Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring
Problem}, DOI={10.1145/3299904.3340311},
booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms}, publisher={Association for Computing Machinery}, author={Bossek,
Jakob and Sudholt, Dirk}, year={2019}, pages={102–115}, collection={FOGA ’19}
}'
chicago: 'Bossek, Jakob, and Dirk Sudholt. “Time Complexity Analysis of RLS and
(1 + 1) EA for the Edge Coloring Problem.” In Proceedings of the 15th ACM/SIGEVO
Conference on Foundations of Genetic Algorithms, 102–115. FOGA ’19. New York,
NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3299904.3340311.'
ieee: 'J. Bossek and D. Sudholt, “Time Complexity Analysis of RLS and (1 + 1) EA
for the Edge Coloring Problem,” in Proceedings of the 15th ACM/SIGEVO Conference
on Foundations of Genetic Algorithms, 2019, pp. 102–115, doi: 10.1145/3299904.3340311.'
mla: Bossek, Jakob, and Dirk Sudholt. “Time Complexity Analysis of RLS and (1 +
1) EA for the Edge Coloring Problem.” Proceedings of the 15th ACM/SIGEVO Conference
on Foundations of Genetic Algorithms, Association for Computing Machinery,
2019, pp. 102–115, doi:10.1145/3299904.3340311.
short: 'J. Bossek, D. Sudholt, in: Proceedings of the 15th ACM/SIGEVO Conference
on Foundations of Genetic Algorithms, Association for Computing Machinery, New
York, NY, USA, 2019, pp. 102–115.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:12Z
department:
- _id: '819'
doi: 10.1145/3299904.3340311
extern: '1'
keyword:
- edge coloring problem
- runtime analysis
language:
- iso: eng
page: 102–115
place: New York, NY, USA
publication: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- 978-1-4503-6254-2
publication_status: published
publisher: Association for Computing Machinery
series_title: FOGA ’19
status: public
title: Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem
type: conference
user_id: '102979'
year: '2019'
...
---
_id: '48875'
abstract:
- lang: eng
text: A multiobjective perspective onto common performance measures such as the
PAR10 score or the expected runtime of single-objective stochastic solvers is
presented by directly investigating the tradeoff between the fraction of failed
runs and the average runtime. Multi-objective indicators operating in the bi-objective
space allow for an overall performance comparison on a set of instances paving
the way for instance-based automated algorithm selection techniques.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos
PM, eds. Learning and Intelligent Optimization. Lecture Notes in Computer
Science. Springer International Publishing; 2019:215–219. doi:10.1007/978-3-030-05348-2_19'
apa: 'Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I.
Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization
(pp. 215–219). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_19'
bibtex: '@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes
in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time}, DOI={10.1007/978-3-030-05348-2_19},
booktitle={Learning and Intelligent Optimization}, publisher={Springer International
Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, Roberto
and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019},
pages={215–219}, collection={Lecture Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” In Learning and Intelligent
Optimization, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and
Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer
International Publishing, 2019. https://doi.org/10.1007/978-3-030-05348-2_19.'
ieee: 'J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization,
2019, pp. 215–219, doi: 10.1007/978-3-030-05348-2_19.'
mla: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” Learning and Intelligent
Optimization, edited by Roberto Battiti et al., Springer International Publishing,
2019, pp. 215–219, doi:10.1007/978-3-030-05348-2_19.'
short: 'J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M.
Pardalos (Eds.), Learning and Intelligent Optimization, Springer International
Publishing, Cham, 2019, pp. 215–219.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:32Z
department:
- _id: '819'
doi: 10.1007/978-3-030-05348-2_19
editor:
- first_name: Roberto
full_name: Battiti, Roberto
last_name: Battiti
- first_name: Mauro
full_name: Brunato, Mauro
last_name: Brunato
- first_name: Ilias
full_name: Kotsireas, Ilias
last_name: Kotsireas
- first_name: Panos M.
full_name: Pardalos, Panos M.
last_name: Pardalos
extern: '1'
keyword:
- Algorithm selection
- Performance measurement
language:
- iso: eng
page: 215–219
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-030-05348-2
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: 'Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected
Running Time'
type: conference
user_id: '102979'
year: '2019'
...
---
_id: '48877'
abstract:
- lang: eng
text: OpenML is an online machine learning platform where researchers can easily
share data, machine learning tasks and experiments as well as organize them online
to work and collaborate more efficiently. In this paper, we present an R package
to interface with the OpenML platform and illustrate its usage in combination
with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5,
2016). We show how the OpenML package allows R users to easily search, download
and upload data sets and machine learning tasks. Furthermore, we also show how
to upload results of experiments, share them with others and download results
from other users. Beyond ensuring reproducibility of results, the OpenML platform
automates much of the drudge work, speeds up research, facilitates collaboration
and increases the users’ visibility online.
author:
- first_name: Giuseppe
full_name: Casalicchio, Giuseppe
last_name: Casalicchio
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Michel
full_name: Lang, Michel
last_name: Lang
- first_name: Dominik
full_name: Kirchhoff, Dominik
last_name: Kirchhoff
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Benjamin
full_name: Hofner, Benjamin
last_name: Hofner
- first_name: Heidi
full_name: Seibold, Heidi
last_name: Seibold
- first_name: Joaquin
full_name: Vanschoren, Joaquin
last_name: Vanschoren
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
citation:
ama: 'Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to
the Machine Learning Platform OpenML. Computational Statistics. 2019;34(3):977–991.
doi:10.1007/s00180-017-0742-2'
apa: 'Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner,
B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R Package
to Connect to the Machine Learning Platform OpenML. Computational Statistics,
34(3), 977–991. https://doi.org/10.1007/s00180-017-0742-2'
bibtex: '@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019,
title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML},
volume={34}, DOI={10.1007/s00180-017-0742-2},
number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe
and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal
and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd},
year={2019}, pages={977–991} }'
chicago: 'Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal
Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl.
“OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” Computational
Statistics 34, no. 3 (2019): 977–991. https://doi.org/10.1007/s00180-017-0742-2.'
ieee: 'G. Casalicchio et al., “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML,” Computational Statistics, vol. 34, no. 3, pp.
977–991, 2019, doi: 10.1007/s00180-017-0742-2.'
mla: 'Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML.” Computational Statistics, vol. 34, no. 3, 2019,
pp. 977–991, doi:10.1007/s00180-017-0742-2.'
short: G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner,
H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:51:17Z
department:
- _id: '819'
doi: 10.1007/s00180-017-0742-2
intvolume: ' 34'
issue: '3'
keyword:
- Databases
- Machine learning
- R
- Reproducible research
language:
- iso: eng
page: 977–991
publication: Computational Statistics
publication_identifier:
issn:
- 0943-4062
status: public
title: 'OpenML: An R Package to Connect to the Machine Learning Platform OpenML'
type: journal_article
user_id: '102979'
volume: 34
year: '2019'
...
---
_id: '46339'
abstract:
- lang: eng
text: 'Evolutionary algorithms have successfully been applied to evolve problem
instances that exhibit a significant difference in performance for a given algorithm
or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP).
Creating a large variety of instances is crucial for successful applications in
the blooming field of algorithm selection. In this paper, we introduce new and
creative mutation operators for evolving instances of the TSP. We show that adopting
those operators in an evolutionary algorithm allows for the generation of benchmark
sets with highly desirable properties: (1) novelty by clear visual distinction
to established benchmark sets in the field, (2) visual and quantitative diversity
in the space of TSP problem characteristics, and (3) significant performance differences
with respect to the restart versions of heuristic state-of-the-art TSP solvers
EAX and LKH. The important aspect of diversity is addressed and achieved solely
by the proposed mutation operators and not enforced by explicit diversity preservation.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving
Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: Friedrich
T, Doerr C, Arnold D, eds. Proceedings of the 15$^th$ ACM/SIGEVO Workshop on
Foundations of Genetic Algorithms (FOGA XV). ; 2019:58–71. doi:10.1145/3299904.3340307'
apa: Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann,
H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation
Operators. In T. Friedrich, C. Doerr, & D. Arnold (Eds.), Proceedings of
the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)
(pp. 58–71). https://doi.org/10.1145/3299904.3340307
bibtex: '@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={Potsdam,
Germany}, title={Evolving Diverse TSP Instances by Means of Novel and Creative
Mutation Operators}, DOI={10.1145/3299904.3340307},
booktitle={Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV)}, author={Bossek, Jakob and Kerschke, Pascal and Neumann,
Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, editor={Friedrich,
Tobias and Doerr, Carola and Arnold, Dirk}, year={2019}, pages={58–71} }'
chicago: Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann,
and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative
Mutation Operators.” In Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations
of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich, Carola Doerr,
and Dirk Arnold, 58–71. Potsdam, Germany, 2019. https://doi.org/10.1145/3299904.3340307.
ieee: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann,
“Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,”
in Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV), 2019, pp. 58–71, doi: 10.1145/3299904.3340307.'
mla: Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and
Creative Mutation Operators.” Proceedings of the 15$^th$ ACM/SIGEVO Workshop
on Foundations of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich
et al., 2019, pp. 58–71, doi:10.1145/3299904.3340307.
short: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann,
in: T. Friedrich, C. Doerr, D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO
Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 2019,
pp. 58–71.'
date_created: 2023-08-04T07:45:39Z
date_updated: 2024-06-10T11:59:26Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3299904.3340307
editor:
- first_name: Tobias
full_name: Friedrich, Tobias
last_name: Friedrich
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Dirk
full_name: Arnold, Dirk
last_name: Arnold
language:
- iso: eng
page: 58–71
place: Potsdam, Germany
publication: Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV)
status: public
title: Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46338'
abstract:
- lang: eng
text: We tackle a bi-objective dynamic orienteering problem where customer requests
arise as time passes by. The goal is to minimize the tour length traveled by a
single delivery vehicle while simultaneously keeping the number of dismissed dynamic
customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm
which is grounded on insights gained from a previous series of work on an a-posteriori
version of the problem, where all request times are known in advance. In our experiments,
we simulate different decision maker strategies and evaluate the development of
the Pareto-front approximations on exemplary problem instances. It turns out,
that despite severely reduced computational budget and no oracle-knowledge of
request times the dynamic EMOA is capable of producing approximations which partially
dominate the results of the a-posteriori EMOA and dynamic integer linear programming
strategies.
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: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering:
Towards a Dynamic Multi-Objective Evolutionary Algorithm. In: Deb K, Goodman E,
Coello CCA, et al., eds. Evolutionary Multi-Criterion Optimization (EMO).
Vol 11411. Lecture Notes in Computer Science. Springer International Publishing;
2019:516–528. doi:10.1007/978-3-030-12598-1_41'
apa: 'Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019).
Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.
In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim,
& P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (Vol.
11411, pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41'
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={East
Lansing, Michigan, USA}, series={Lecture Notes in Computer Science}, title={Bi-Objective
Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}, volume={11411},
DOI={10.1007/978-3-030-12598-1_41},
booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer
International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel,
Stephan and Rudolph, Günter and Trautmann, Heike}, editor={Deb, Kalyanmoy and
Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen,
Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={516–528}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike
Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary
Algorithm.” In Evolutionary Multi-Criterion Optimization (EMO), edited
by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa
Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:516–528. Lecture Notes in
Computer Science. East Lansing, Michigan, USA: Springer International Publishing,
2019. https://doi.org/10.1007/978-3-030-12598-1_41.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective
Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm,” in Evolutionary
Multi-Criterion Optimization (EMO), 2019, vol. 11411, pp. 516–528, doi: 10.1007/978-3-030-12598-1_41.'
mla: 'Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective
Evolutionary Algorithm.” Evolutionary Multi-Criterion Optimization (EMO),
edited by Kalyanmoy Deb et al., vol. 11411, Springer International Publishing,
2019, pp. 516–528, doi:10.1007/978-3-030-12598-1_41.'
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E.
Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.),
Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing,
East Lansing, Michigan, USA, 2019, pp. 516–528.'
date_created: 2023-08-04T07:44:59Z
date_updated: 2024-06-10T12:00:05Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-12598-1_41
editor:
- first_name: Kalyanmoy
full_name: Deb, Kalyanmoy
last_name: Deb
- first_name: Erik
full_name: Goodman, Erik
last_name: Goodman
- first_name: Coello Carlos A.
full_name: Coello, Coello Carlos A.
last_name: Coello
- first_name: Kathrin
full_name: Klamroth, Kathrin
last_name: Klamroth
- first_name: Kaisa
full_name: Miettinen, Kaisa
last_name: Miettinen
- first_name: Sanaz
full_name: Mostaghim, Sanaz
last_name: Mostaghim
- first_name: Patrick
full_name: Reed, Patrick
last_name: Reed
intvolume: ' 11411'
language:
- iso: eng
page: 516–528
place: East Lansing, Michigan, USA
publication: Evolutionary Multi-Criterion Optimization (EMO)
publication_identifier:
isbn:
- 978-3-030-12597-4
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: 'Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary
Algorithm'
type: conference
user_id: '15504'
volume: 11411
year: '2019'
...
---
_id: '46337'
abstract:
- lang: eng
text: A multiobjective perspective onto common performance measures such as the
PAR10 score or the expected runtime of single-objective stochastic solvers is
presented by directly investigating the tradeoff between the fraction of failed
runs and the average runtime. Multi-objective indicators operating in the bi-objective
space allow for an overall performance comparison on a set of instances paving
the way for instance-based automated algorithm selection techniques.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos
P, eds. Learning and Intelligent Optimization. Vol 11353. Lecture Notes
in Computer Science. Springer; 2019:215–219.'
apa: 'Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I.
Kotsireas, & P. Pardalos (Eds.), Learning and Intelligent Optimization
(Vol. 11353, pp. 215–219). Springer.'
bibtex: '@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes
in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time}, volume={11353}, booktitle={Learning and Intelligent
Optimization}, publisher={Springer}, author={Bossek, Jakob and Trautmann, Heike},
editor={Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}, year={2019},
pages={215–219}, collection={Lecture Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” In Learning and Intelligent
Optimization, edited by R Battiti, M Brunato, I Kotsireas, and P Pardalos,
11353:215–219. Lecture Notes in Computer Science. Cham: Springer, 2019.'
ieee: 'J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization,
2019, vol. 11353, pp. 215–219.'
mla: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” Learning and Intelligent
Optimization, edited by R Battiti et al., vol. 11353, Springer, 2019, pp.
215–219.'
short: 'J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P. Pardalos
(Eds.), Learning and Intelligent Optimization, Springer, Cham, 2019, pp. 215–219.'
date_created: 2023-08-04T07:44:10Z
date_updated: 2024-06-10T12:00:23Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: R
full_name: Battiti, R
last_name: Battiti
- first_name: M
full_name: Brunato, M
last_name: Brunato
- first_name: I
full_name: Kotsireas, I
last_name: Kotsireas
- first_name: P
full_name: Pardalos, P
last_name: Pardalos
intvolume: ' 11353'
language:
- iso: eng
page: 215–219
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-030-05347-5
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected
Running Time'
type: conference
user_id: '15504'
volume: 11353
year: '2019'
...