---
_id: '63053'
author:
- first_name: Carlos
full_name: Hernández, Carlos
last_name: Hernández
- first_name: Angel E.
full_name: Rodriguez-Fernandez, Angel E.
last_name: Rodriguez-Fernandez
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Oliver
full_name: Cuate, Oliver
last_name: Cuate
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
citation:
ama: Hernández C, Rodriguez-Fernandez AE, Schäpermeier L, Cuate O, Trautmann H,
Schütze O. An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization. IEEE Transactions on Evolutionary
Computation. Published online 2025:1-1. doi:10.1109/TEVC.2025.3637276
apa: Hernández, C., Rodriguez-Fernandez, A. E., Schäpermeier, L., Cuate, O., Trautmann,
H., & Schütze, O. (2025). An Evolutionary Approach for the Computation of
∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization. IEEE
Transactions on Evolutionary Computation, 1–1. https://doi.org/10.1109/TEVC.2025.3637276
bibtex: '@article{Hernández_Rodriguez-Fernandez_Schäpermeier_Cuate_Trautmann_Schütze_2025,
title={An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization}, DOI={10.1109/TEVC.2025.3637276},
journal={IEEE Transactions on Evolutionary Computation}, author={Hernández, Carlos
and Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Cuate, Oliver
and Trautmann, Heike and Schütze, Oliver}, year={2025}, pages={1–1} }'
chicago: Hernández, Carlos, Angel E. Rodriguez-Fernandez, Lennart Schäpermeier,
Oliver Cuate, Heike Trautmann, and Oliver Schütze. “An Evolutionary Approach for
the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal
Optimization.” IEEE Transactions on Evolutionary Computation, 2025, 1–1.
https://doi.org/10.1109/TEVC.2025.3637276.
ieee: 'C. Hernández, A. E. Rodriguez-Fernandez, L. Schäpermeier, O. Cuate, H. Trautmann,
and O. Schütze, “An Evolutionary Approach for the Computation of ∈-Locally Optimal
Solutions for Multi-Objective Multimodal Optimization,” IEEE Transactions on
Evolutionary Computation, pp. 1–1, 2025, doi: 10.1109/TEVC.2025.3637276.'
mla: Hernández, Carlos, et al. “An Evolutionary Approach for the Computation of
∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization.” IEEE
Transactions on Evolutionary Computation, 2025, pp. 1–1, doi:10.1109/TEVC.2025.3637276.
short: C. Hernández, A.E. Rodriguez-Fernandez, L. Schäpermeier, O. Cuate, H. Trautmann,
O. Schütze, IEEE Transactions on Evolutionary Computation (2025) 1–1.
date_created: 2025-12-12T06:13:06Z
date_updated: 2025-12-12T06:13:51Z
department:
- _id: '819'
doi: 10.1109/TEVC.2025.3637276
keyword:
- Optimization
- Evolutionary computation
- Hands
- Proposals
- Convergence
- Computational efficiency
- Artificial intelligence
- Accuracy
- Approximation algorithms
- Aerospace electronics
- Multi-objective optimization
- evolutionary algorithms
- nearly optimal solutions
- multimodal optimization
- archiving
- continuation
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization
type: journal_article
user_id: '15504'
year: '2025'
...
---
_id: '54548'
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Prager RP, Trautmann H. Exploratory Landscape Analysis for Mixed-Variable Problems.
IEEE Transactions on Evolutionary Computation. Published online 2024:1-1.
doi:10.1109/TEVC.2024.3399560
apa: Prager, R. P., & Trautmann, H. (2024). Exploratory Landscape Analysis for
Mixed-Variable Problems. IEEE Transactions on Evolutionary Computation,
1–1. https://doi.org/10.1109/TEVC.2024.3399560
bibtex: '@article{Prager_Trautmann_2024, title={Exploratory Landscape Analysis for
Mixed-Variable Problems}, DOI={10.1109/TEVC.2024.3399560},
journal={IEEE Transactions on Evolutionary Computation}, author={Prager, Raphael
Patrick and Trautmann, Heike}, year={2024}, pages={1–1} }'
chicago: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
for Mixed-Variable Problems.” IEEE Transactions on Evolutionary Computation,
2024, 1–1. https://doi.org/10.1109/TEVC.2024.3399560.
ieee: 'R. P. Prager and H. Trautmann, “Exploratory Landscape Analysis for Mixed-Variable
Problems,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2024,
doi: 10.1109/TEVC.2024.3399560.'
mla: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
for Mixed-Variable Problems.” IEEE Transactions on Evolutionary Computation,
2024, pp. 1–1, doi:10.1109/TEVC.2024.3399560.
short: R.P. Prager, H. Trautmann, IEEE Transactions on Evolutionary Computation
(2024) 1–1.
date_created: 2024-06-03T06:16:33Z
date_updated: 2024-06-03T06:17:13Z
department:
- _id: '819'
doi: 10.1109/TEVC.2024.3399560
keyword:
- Optimization
- Evolutionary computation
- Benchmark testing
- Hyperparameter optimization
- Portfolios
- Extraterrestrial measurements
- Dispersion
- Exploratory landscape analysis
- mixed-variable problem
- mixed search spaces
- automated algorithm selection
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: Exploratory Landscape Analysis for Mixed-Variable Problems
type: journal_article
user_id: '15504'
year: '2024'
...
---
_id: '56221'
author:
- first_name: Angel E.
full_name: Rodriguez-Fernandez, Angel E.
last_name: Rodriguez-Fernandez
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Carlos
full_name: Hernández, Carlos
last_name: Hernández
- 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
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
citation:
ama: Rodriguez-Fernandez AE, Schäpermeier L, Hernández C, Kerschke P, Trautmann
H, Schütze O. Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal
Optimization. IEEE Transactions on Evolutionary Computation. Published
online 2024:1-1. doi:10.1109/TEVC.2024.3458855
apa: Rodriguez-Fernandez, A. E., Schäpermeier, L., Hernández, C., Kerschke, P.,
Trautmann, H., & Schütze, O. (2024). Finding ϵ-Locally Optimal Solutions for
Multi-Objective Multimodal Optimization. IEEE Transactions on Evolutionary
Computation, 1–1. https://doi.org/10.1109/TEVC.2024.3458855
bibtex: '@article{Rodriguez-Fernandez_Schäpermeier_Hernández_Kerschke_Trautmann_Schütze_2024,
title={Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization},
DOI={10.1109/TEVC.2024.3458855},
journal={IEEE Transactions on Evolutionary Computation}, author={Rodriguez-Fernandez,
Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal
and Trautmann, Heike and Schütze, Oliver}, year={2024}, pages={1–1} }'
chicago: Rodriguez-Fernandez, Angel E., Lennart Schäpermeier, Carlos Hernández,
Pascal Kerschke, Heike Trautmann, and Oliver Schütze. “Finding ϵ-Locally Optimal
Solutions for Multi-Objective Multimodal Optimization.” IEEE Transactions on
Evolutionary Computation, 2024, 1–1. https://doi.org/10.1109/TEVC.2024.3458855.
ieee: 'A. E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H.
Trautmann, and O. Schütze, “Finding ϵ-Locally Optimal Solutions for Multi-Objective
Multimodal Optimization,” IEEE Transactions on Evolutionary Computation,
pp. 1–1, 2024, doi: 10.1109/TEVC.2024.3458855.'
mla: Rodriguez-Fernandez, Angel E., et al. “Finding ϵ-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization.” IEEE Transactions on Evolutionary
Computation, 2024, pp. 1–1, doi:10.1109/TEVC.2024.3458855.
short: A.E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H.
Trautmann, O. Schütze, IEEE Transactions on Evolutionary Computation (2024) 1–1.
date_created: 2024-09-24T08:01:14Z
date_updated: 2024-09-24T08:01:47Z
doi: 10.1109/TEVC.2024.3458855
keyword:
- Optimization
- Evolutionary computation
- Approximation algorithms
- Benchmark testing
- Vectors
- Surveys
- Pareto optimization
- multi-objective optimization
- evolutionary computation
- multimodal optimization
- local solutions
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization
type: journal_article
user_id: '15504'
year: '2024'
...
---
_id: '48886'
abstract:
- lang: eng
text: 'Generating new instances via evolutionary methods is commonly used to create
new benchmarking data-sets, with a focus on attempting to cover an instance-space
as completely as possible. Recent approaches have exploited Quality-Diversity
methods to evolve sets of instances that are both diverse and discriminatory with
respect to a portfolio of solvers, but these methods can be challenging when attempting
to find diversity in a high-dimensional feature-space. We address this issue by
training a model based on Principal Component Analysis on existing instances to
create a low-dimension projection of the high-dimension feature-vectors, and then
apply Novelty Search directly in the new low-dimension space. We conduct experiments
to evolve diverse and discriminatory instances of Knapsack Problems, comparing
the use of Novelty Search in the original feature-space to using Novelty Search
in a low-dimensional projection, and repeat over a given set of dimensions. We
find that the methods are complementary: if treated as an ensemble, they collectively
provide increased coverage of the space. Specifically, searching for novelty in
a low-dimension space contributes 56% of the filled regions of the space, while
searching directly in the feature-space covers the remaining 44%.'
author:
- first_name: Alejandro
full_name: Marrero, Alejandro
last_name: Marrero
- first_name: Eduardo
full_name: Segredo, Eduardo
last_name: Segredo
- first_name: Emma
full_name: Hart, Emma
last_name: Hart
- 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
citation:
ama: 'Marrero A, Segredo E, Hart E, Bossek J, Neumann A. Generating Diverse and
Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions
of Feature-Space. In: Proceedings of the Genetic} and Evolutionary Computation
Conference. GECCO’23. Association for Computing Machinery; 2023:312–320. doi:10.1145/3583131.3590504'
apa: Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023). Generating
Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable
Dimensions of Feature-Space. Proceedings of the Genetic} and Evolutionary Computation
Conference, 312–320. https://doi.org/10.1145/3583131.3590504
bibtex: '@inproceedings{Marrero_Segredo_Hart_Bossek_Neumann_2023, place={New York,
NY, USA}, series={GECCO’23}, title={Generating Diverse and Discriminatory Knapsack
Instances by Searching for Novelty in Variable Dimensions of Feature-Space}, DOI={10.1145/3583131.3590504}, booktitle={Proceedings
of the Genetic} and Evolutionary Computation Conference}, publisher={Association
for Computing Machinery}, author={Marrero, Alejandro and Segredo, Eduardo and
Hart, Emma and Bossek, Jakob and Neumann, Aneta}, year={2023}, pages={312–320},
collection={GECCO’23} }'
chicago: 'Marrero, Alejandro, Eduardo Segredo, Emma Hart, Jakob Bossek, and Aneta
Neumann. “Generating Diverse and Discriminatory Knapsack Instances by Searching
for Novelty in Variable Dimensions of Feature-Space.” In Proceedings of the
Genetic} and Evolutionary Computation Conference, 312–320. GECCO’23. New York,
NY, USA: Association for Computing Machinery, 2023. https://doi.org/10.1145/3583131.3590504.'
ieee: 'A. Marrero, E. Segredo, E. Hart, J. Bossek, and A. Neumann, “Generating Diverse
and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions
of Feature-Space,” in Proceedings of the Genetic} and Evolutionary Computation
Conference, 2023, pp. 312–320, doi: 10.1145/3583131.3590504.'
mla: Marrero, Alejandro, et al. “Generating Diverse and Discriminatory Knapsack
Instances by Searching for Novelty in Variable Dimensions of Feature-Space.” Proceedings
of the Genetic} and Evolutionary Computation Conference, Association for Computing
Machinery, 2023, pp. 312–320, doi:10.1145/3583131.3590504.
short: 'A. Marrero, E. Segredo, E. Hart, J. Bossek, A. Neumann, in: Proceedings
of the Genetic} and Evolutionary Computation Conference, Association for Computing
Machinery, New York, NY, USA, 2023, pp. 312–320.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:49:32Z
department:
- _id: '819'
doi: 10.1145/3583131.3590504
extern: '1'
keyword:
- evolutionary computation
- instance generation
- instance-space analysis
- knapsack problem
- novelty search
language:
- iso: eng
page: 312–320
place: New York, NY, USA
publication: Proceedings of the Genetic} and Evolutionary Computation Conference
publication_identifier:
isbn:
- '9798400701191'
publisher: Association for Computing Machinery
series_title: GECCO’23
status: public
title: Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty
in Variable Dimensions of Feature-Space
type: conference
user_id: '102979'
year: '2023'
...
---
_id: '48857'
abstract:
- lang: eng
text: 'While finding minimum-cost spanning trees (MST) in undirected graphs is solvable
in polynomial time, the multi-criteria minimum spanning tree problem (mcMST) is
NP-hard. Interestingly, the mcMST problem has not been in focus of evolutionary
computation research for a long period of time, although, its relevance for real
world problems is easy to see. The available and most notable approaches by Zhou
and Gen as well as by Knowles and Corne concentrate on solution encoding and on
fairly dated selection mechanisms. In this work, we revisit the mcMST and focus
on the mutation operators as exploratory components of evolutionary algorithms
neglected so far. We investigate optimal solution characteristics to discuss current
mutation strategies, identify shortcomings of these operators, and propose a sub-tree
based operator which offers what we term Pareto-beneficial behavior: ensuring
convergence and diversity at the same time. The operator is empirically evaluated
inside modern standard evolutionary meta-heuristics for multi-criteria optimization
and compared to hitherto applied mutation operators in the context of mcMST.'
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. A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria
Minimum Spanning Tree Problem. In: 2017 IEEE Symposium Series on Computational
Intelligence (SSCI). ; 2017:1–8. doi:10.1109/SSCI.2017.8285183'
apa: Bossek, J., & Grimme, C. (2017). A Pareto-Beneficial Sub-Tree Mutation
for the Multi-Criteria Minimum Spanning Tree Problem. 2017 IEEE Symposium Series
on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285183
bibtex: '@inproceedings{Bossek_Grimme_2017, title={A Pareto-Beneficial Sub-Tree
Mutation for the Multi-Criteria Minimum Spanning Tree Problem}, DOI={10.1109/SSCI.2017.8285183},
booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Bossek,
Jakob and Grimme, Christian}, year={2017}, pages={1–8} }'
chicago: Bossek, Jakob, and Christian Grimme. “A Pareto-Beneficial Sub-Tree Mutation
for the Multi-Criteria Minimum Spanning Tree Problem.” In 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 1–8, 2017. https://doi.org/10.1109/SSCI.2017.8285183.
ieee: 'J. Bossek and C. Grimme, “A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria
Minimum Spanning Tree Problem,” in 2017 IEEE Symposium Series on Computational
Intelligence (SSCI), 2017, pp. 1–8, doi: 10.1109/SSCI.2017.8285183.'
mla: Bossek, Jakob, and Christian Grimme. “A Pareto-Beneficial Sub-Tree Mutation
for the Multi-Criteria Minimum Spanning Tree Problem.” 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 2017, pp. 1–8, doi:10.1109/SSCI.2017.8285183.
short: 'J. Bossek, C. Grimme, in: 2017 IEEE Symposium Series on Computational Intelligence
(SSCI), 2017, pp. 1–8.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:28Z
department:
- _id: '819'
doi: 10.1109/SSCI.2017.8285183
extern: '1'
keyword:
- Convergence
- Encoding
- Euclidean distance
- Evolutionary computation
- Heating systems
- Optimization
- Standards
language:
- iso: eng
page: 1–8
publication: 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
publication_status: published
status: public
title: A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning
Tree Problem
type: conference
user_id: '102979'
year: '2017'
...
---
_id: '48856'
abstract:
- lang: eng
text: There exist many optimal or heuristic priority rules for machine scheduling
problems, which can easily be integrated into single-objective evolutionary algorithms
via mutation operators. However, in the multi-objective case, simultaneously applying
different priorities for different objectives may cause severe disruptions in
the genome and may lead to inferior solutions. In this paper, we combine an existing
mutation operator concept with new insights from detailed observation of the structure
of solutions for multi-objective machine scheduling problems. This allows the
comprehensive integration of priority rules to produce better Pareto-front approximations.
We evaluate the extended operator concept compared to standard swap mutation and
the stand-alone components of our hybrid scheme, which performs best in all evaluated
cases.
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. An Extended Mutation-Based Priority-Rule Integration Concept
for Multi-Objective Machine Scheduling. In: 2017 IEEE Symposium Series on Computational
Intelligence (SSCI). ; 2017:1–8. doi:10.1109/SSCI.2017.8285224'
apa: Bossek, J., & Grimme, C. (2017). An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling. 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285224
bibtex: '@inproceedings{Bossek_Grimme_2017, title={An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling}, DOI={10.1109/SSCI.2017.8285224},
booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Bossek,
Jakob and Grimme, Christian}, year={2017}, pages={1–8} }'
chicago: Bossek, Jakob, and Christian Grimme. “An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling.” In 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 1–8, 2017. https://doi.org/10.1109/SSCI.2017.8285224.
ieee: 'J. Bossek and C. Grimme, “An Extended Mutation-Based Priority-Rule Integration
Concept for Multi-Objective Machine Scheduling,” in 2017 IEEE Symposium Series
on Computational Intelligence (SSCI), 2017, pp. 1–8, doi: 10.1109/SSCI.2017.8285224.'
mla: Bossek, Jakob, and Christian Grimme. “An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling.” 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 2017, pp. 1–8, doi:10.1109/SSCI.2017.8285224.
short: 'J. Bossek, C. Grimme, in: 2017 IEEE Symposium Series on Computational Intelligence
(SSCI), 2017, pp. 1–8.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:36Z
department:
- _id: '819'
doi: 10.1109/SSCI.2017.8285224
extern: '1'
keyword:
- Evolutionary computation
- Processor scheduling
- Schedules
- Scheduling
- Sociology
- Standards
- Statistics
language:
- iso: eng
page: 1–8
publication: 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
publication_status: published
status: public
title: An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective
Machine Scheduling
type: conference
user_id: '102979'
year: '2017'
...