---
_id: '48847'
abstract:
- lang: eng
text: Dynamic optimization problems have gained significant attention in evolutionary
computation as evolutionary algorithms (EAs) can easily adapt to changing environments.
We show that EAs can solve the graph coloring problem for bipartite graphs more
efficiently by using dynamic optimization. In our approach the graph instance
is given incrementally such that the EA can reoptimize its coloring when a new
edge introduces a conflict. We show that, when edges are inserted in a way that
preserves graph connectivity, Randomized Local Search (RLS) efficiently finds
a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS
and other EAs need exponential expected time in a static optimization scenario.
We investigate different ways of building up the graph by popular graph traversals
such as breadth-first-search and depth-first-search and analyse the resulting
runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$})
RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using
3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite
graph, outperforming offspring populations. This is the first example where an
island model guarantees a speedup that is not bounded in the number of islands.
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. More Effective Randomized Search Heuristics
for Graph Coloring through Dynamic Optimization. In: Proceedings of the Genetic
and Evolutionary Computation Conference. GECCO ’20. Association for Computing
Machinery; 2020:1277–1285. doi:10.1145/3377930.3390174'
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.
Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285.
https://doi.org/10.1145/3377930.3390174
bibtex: '@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA},
series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization}, DOI={10.1145/3377930.3390174},
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={2020}, pages={1277–1285}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery,
2020. https://doi.org/10.1145/3377930.3390174.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized
Search Heuristics for Graph Coloring through Dynamic Optimization,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2020, pp. 1277–1285,
doi: 10.1145/3377930.3390174.'
mla: Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2020, pp. 1277–1285,
doi:10.1145/3377930.3390174.
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, 2020, pp. 1277–1285.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:41Z
department:
- _id: '819'
doi: 10.1145/3377930.3390174
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1277–1285
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: More Effective Randomized Search Heuristics for Graph Coloring through Dynamic
Optimization
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48851'
abstract:
- lang: eng
text: Several important optimization problems in the area of vehicle routing can
be seen as variants of the classical Traveling Salesperson Problem (TSP). In the
area of evolutionary computation, the Traveling Thief Problem (TTP) has gained
increasing interest over the last 5 years. In this paper, we investigate the effect
of weights on such problems, in the sense that the cost of traveling increases
with respect to the weights of nodes already visited during a tour. This provides
abstractions of important TSP variants such as the Traveling Thief Problem and
time dependent TSP variants, and allows to study precisely the increase in difficulty
caused by weight dependence. We provide a 3.59-approximation for this weight dependent
version of TSP with metric distances and bounded positive weights. Furthermore,
we conduct experimental investigations for simple randomized local search with
classical mutation operators and two variants of the state-of-the-art evolutionary
algorithm EAX adapted to the weighted TSP. Our results show the impact of the
node weights on the position of the nodes in the resulting tour.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Katrin
full_name: Casel, Katrin
last_name: Casel
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling
Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:10.1145/3377930.3390243'
apa: 'Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight
Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized
Search Heuristics. Proceedings of the Genetic and Evolutionary Computation
Conference, 1286–1294. https://doi.org/10.1145/3377930.3390243'
bibtex: '@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY,
USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson
Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={10.1145/3377930.3390243},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel,
Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294},
collection={GECCO ’20} }'
chicago: 'Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The
Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 1286–1294. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390243.'
ieee: 'J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent
Traveling Salesperson Problem: Approximation Algorithms and Randomized Search
Heuristics,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 1286–1294, doi: 10.1145/3377930.3390243.'
mla: 'Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem:
Approximation Algorithms and Randomized Search Heuristics.” Proceedings of
the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243.'
short: 'J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1286–1294.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:33Z
department:
- _id: '819'
doi: 10.1145/3377930.3390243
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1286–1294
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48845'
abstract:
- lang: eng
text: In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems
(VRPs) often imply repeated decision making on dynamic customer requests. As in
classical VRPs, tours have to be planned short while the number of serviced customers
has to be maximized at the same time resulting in a multi-objective problem. Beyond
that, however, dynamic requests lead to the need for re-planning of not yet realized
tour parts, while already realized tour parts are irreversible. In this paper
we study this type of bi-objective dynamic VRP including sequential decision making
and concurrent realization of decisions. We adopt a recently proposed Dynamic
Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend
it to the more realistic (here considered) scenario of multiple vehicles. We empirically
show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant
variant of the proposed DEMOA as well as with the dynamic single-vehicle approach
proposed earlier.
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: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple
Vehicles. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:10.1145/3377930.3390146'
apa: Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing
of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation
Conference, 166–174. https://doi.org/10.1145/3377930.3390146
bibtex: '@inproceedings{Bossek_Grimme_Trautmann_2020, place={New York, NY, USA},
series={GECCO ’20}, title={Dynamic Bi-Objective Routing of Multiple Vehicles},
DOI={10.1145/3377930.3390146},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Trautmann, Heike}, year={2020}, pages={166–174}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective
Routing of Multiple Vehicles.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 166–174. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390146.'
ieee: 'J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of
Multiple Vehicles,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2020, pp. 166–174, doi: 10.1145/3377930.3390146.'
mla: Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.”
Proceedings of the Genetic and Evolutionary Computation Conference, Association
for Computing Machinery, 2020, pp. 166–174, doi:10.1145/3377930.3390146.
short: 'J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 166–174.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:24Z
department:
- _id: '819'
doi: 10.1145/3377930.3390146
extern: '1'
keyword:
- decision making
- dynamic optimization
- evolutionary algorithms
- multi-objective optimization
- vehicle routing
language:
- iso: eng
page: 166–174
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: Dynamic Bi-Objective Routing of Multiple Vehicles
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48850'
abstract:
- lang: eng
text: Sequential model-based optimization (SMBO) approaches are algorithms for solving
problems that require computationally or otherwise expensive function evaluations.
The key design principle of SMBO is a substitution of the true objective function
by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO
algorithms are intrinsically modular, leaving the user with many important design
choices. Significant research efforts go into understanding which settings perform
best for which type of problems. Most works, however, focus on the choice of the
model, the acquisition function, and the strategy used to optimize the latter.
The choice of the initial sampling strategy, however, receives much less attention.
Not surprisingly, quite diverging recommendations can be found in the literature.
We analyze in this work how the size and the distribution of the initial sample
influences the overall quality of the efficient global optimization (EGO) algorithm,
a well-known SMBO approach. While, overall, small initial budgets using Halton
sampling seem preferable, we also observe that the performance landscape is rather
unstructured. We furthermore identify several situations in which EGO performs
unfavorably against random sampling. Both observations indicate that an adaptive
SMBO design could be beneficial, making SMBO an interesting test-bed for automated
algorithm design.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Bossek J, Doerr C, Kerschke P. Initial Design Strategies and Their Effects
on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:10.1145/3377930.3390155'
apa: 'Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB. Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. https://doi.org/10.1145/3377930.3390155'
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_2020, place={New York, NY, USA}, series={GECCO
’20}, title={Initial Design Strategies and Their Effects on Sequential Model-Based
Optimization: An Exploratory Case Study Based on BBOB}, DOI={10.1145/3377930.3390155},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Doerr,
Carola and Kerschke, Pascal}, year={2020}, pages={778–786}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Carola Doerr, and Pascal Kerschke. “Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB.” In Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. GECCO ’20. New York, NY, USA: Association for Computing
Machinery, 2020. https://doi.org/10.1145/3377930.3390155.'
ieee: 'J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their
Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based
on BBOB,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 778–786, doi: 10.1145/3377930.3390155.'
mla: 'Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential
Model-Based Optimization: An Exploratory Case Study Based on BBOB.” Proceedings
of the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 778–786, doi:10.1145/3377930.3390155.'
short: 'J. Bossek, C. Doerr, P. Kerschke, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 778–786.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:44:01Z
department:
- _id: '819'
doi: 10.1145/3377930.3390155
extern: '1'
keyword:
- continuous black-box optimization
- design of experiments
- initial design
- sequential model-based optimization
language:
- iso: eng
page: 778–786
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'Initial Design Strategies and Their Effects on Sequential Model-Based Optimization:
An Exploratory Case Study Based on BBOB'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48879'
abstract:
- lang: eng
text: Evolving diverse sets of high quality solutions has gained increasing interest
in the evolutionary computation literature in recent years. With this paper, we
contribute to this area of research by examining evolutionary diversity optimisation
approaches for the classical Traveling Salesperson Problem (TSP). We study the
impact of using different diversity measures for a given set of tours and the
ability of evolutionary algorithms to obtain a diverse set of high quality solutions
when adopting these measures. Our studies show that a large variety of diverse
high quality tours can be achieved by using our approaches. Furthermore, we compare
our approaches in terms of theoretical properties and the final set of tours obtained
by the evolutionary diversity optimisation algorithm.
author:
- first_name: Anh Viet
full_name: Do, Anh Viet
last_name: Do
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for
the Travelling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO’20. Association for Computing Machinery; 2020:681–689.
doi:10.1145/3377930.3389844'
apa: Do, A. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse
Sets of Tours for the Travelling Salesperson Problem. Proceedings of the Genetic
and Evolutionary Computation Conference, 681–689. https://doi.org/10.1145/3377930.3389844
bibtex: '@inproceedings{Do_Bossek_Neumann_Neumann_2020, place={New York, NY, USA},
series={GECCO’20}, title={Evolving Diverse Sets of Tours for the Travelling Salesperson
Problem}, DOI={10.1145/3377930.3389844},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Do, Anh Viet and Bossek,
Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={681–689}, collection={GECCO’20}
}'
chicago: 'Do, Anh Viet, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Evolving
Diverse Sets of Tours for the Travelling Salesperson Problem.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 681–689. GECCO’20.
New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3389844.'
ieee: 'A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of
Tours for the Travelling Salesperson Problem,” in Proceedings of the Genetic
and Evolutionary Computation Conference, 2020, pp. 681–689, doi: 10.1145/3377930.3389844.'
mla: Do, Anh Viet, et al. “Evolving Diverse Sets of Tours for the Travelling Salesperson
Problem.” Proceedings of the Genetic and Evolutionary Computation Conference,
Association for Computing Machinery, 2020, pp. 681–689, doi:10.1145/3377930.3389844.
short: 'A.V. Do, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 681–689.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:48:50Z
department:
- _id: '819'
doi: 10.1145/3377930.3389844
extern: '1'
keyword:
- diversity maximisation
- evolutionary algorithms
- travelling salesperson problem
language:
- iso: eng
page: 681–689
place: New York, NY, USA
publication: Proceedings of the 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: Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
type: conference
user_id: '102979'
year: '2020'
...
---
_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'
...