Below is a list of publications that I was involved in.
Feel free to contact me if you have any questions, feedback or comments about the presented research, preferably via email:
martin.zaefferer (at) gmx.de
86 entries « ‹ 1 of 2
› » 2023
Bartz-Beielstein, Thomas; Chandrasekaran, Sowmya; Rehbach, Frederik; Zaefferer, Martin
Case Study I: Tuning Random Forest (Ranger) Book Section
In: Hyperparameter Tuning for Machine and Deep Learning with R, pp. 187–220, Springer, 2023.
@incollection{BartzBeielstein2023b,
title = {Case Study I: Tuning Random Forest (Ranger)},
author = {Thomas Bartz-Beielstein and Sowmya Chandrasekaran and Frederik Rehbach and Martin Zaefferer},
doi = {10.1007/978-981-19-5170-1_8},
year = {2023},
date = {2023-01-01},
booktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},
pages = {187–220},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Zaefferer, Martin; Chandrasekaran, Sowmya
Case Study IV: Tuned Reinforcement Learning (in Python) Book Section
In: Hyperparameter Tuning for Machine and Deep Learning with R, pp. 271–281, Springer, 2023.
@incollection{Zaefferer2023a,
title = {Case Study IV: Tuned Reinforcement Learning (in Python)},
author = {Martin Zaefferer and Sowmya Chandrasekaran},
doi = {10.1007/978-981-19-5170-1_11},
year = {2023},
date = {2023-01-01},
booktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},
pages = {271–281},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas
Global Study: Influence of Tuning Book Section
In: Hyperparameter Tuning for Machine and Deep Learning with R, pp. 283–301, Springer, 2023.
@incollection{Zaefferer2023,
title = {Global Study: Influence of Tuning},
author = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},
doi = {10.1007/978-981-19-5170-1_12},
year = {2023},
date = {2023-01-01},
booktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},
pages = {283–301},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Bartz-Beielstein, Thomas; Zaefferer, Martin
Hyperparameter Tuning Approaches Book Section
In: Hyperparameter Tuning for Machine and Deep Learning with R, pp. 71–119, Springer, 2023.
@incollection{BartzBeielstein2023a,
title = {Hyperparameter Tuning Approaches},
author = {Thomas Bartz-Beielstein and Martin Zaefferer},
doi = {10.1007/978-981-19-5170-1_4},
year = {2023},
date = {2023-01-01},
booktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},
pages = {71–119},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)
Hyperparameter Tuning for Machine and Deep Learning with R Book
Springer, 2023.
@book{Bartz2023,
title = {Hyperparameter Tuning for Machine and Deep Learning with R},
editor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},
doi = {10.1007/978-981-19-5170-1},
year = {2023},
date = {2023-01-01},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Bartz-Beielstein, Thomas; Zaefferer, Martin
Models Book Section
In: Hyperparameter Tuning for Machine and Deep Learning with R, pp. 27–69, Springer, 2023.
@incollection{BartzBeielstein2023c,
title = {Models},
author = {Thomas Bartz-Beielstein and Martin Zaefferer},
doi = {10.1007/978-981-19-5170-1_3},
year = {2023},
date = {2023-01-01},
booktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},
pages = {27–69},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin
Quantum computer based Feature Selection in Machine Learning Journal Article
In: 2023.
@article{Hellstern2023,
title = {Quantum computer based Feature Selection in Machine Learning},
author = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},
doi = {10.48550/ARXIV.2306.10591},
year = {2023},
date = {2023-06-01},
publisher = {arXiv},
abstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.
Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Daniël; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, André; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; López-Ibáñez, Manuel; Irurozki, Ekhine
The first AI4TSP competition: Learning to solve stochastic routing problems Journal Article
In: Artificial Intelligence, vol. 319, pp. 103918, 2023, ISSN: 0004-3702.
@article{Zhang2023,
title = {The first AI4TSP competition: Learning to solve stochastic routing problems},
author = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Daniël Vos and Sicco Verwer and Fynn Schmitt-Ulms and André Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel López-Ibáñez and Ekhine Irurozki},
doi = {10.1016/j.artint.2023.103918},
issn = {0004-3702},
year = {2023},
date = {2023-06-01},
journal = {Artificial Intelligence},
volume = {319},
pages = {103918},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf
Tuning: Methodology Book Section
In: Hyperparameter Tuning for Machine and Deep Learning with R, pp. 7–26, Springer, 2023.
@incollection{BartzBeielstein2023,
title = {Tuning: Methodology},
author = {Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},
doi = {10.1007/978-981-19-5170-1_2},
year = {2023},
date = {2023-01-01},
booktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},
pages = {7–26},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2022
Rehbach, Frederik; Zaefferer, Martin; Fischbach, Andreas; Rudolph, Günter; Bartz-Beielstein, Thomas
Benchmark-Driven Algorithm Configuration Applied to Parallel Model-Based Optimization Journal Article
In: techrxiv preprint, 2022.
@article{Rehbach2022a,
title = {Benchmark-Driven Algorithm Configuration Applied to Parallel Model-Based Optimization},
author = {Frederik Rehbach and Martin Zaefferer and Andreas Fischbach and Günter Rudolph and Thomas Bartz-Beielstein},
doi = {10.36227/techrxiv.18999767.v1},
year = {2022},
date = {2022-01-01},
journal = {techrxiv preprint},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rehbach, Frederik; Zaefferer, Martin; Fischbach, Andreas; Rudolph, Gunter; Bartz-Beielstein, Thomas
Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm Journal Article
In: IEEE Transactions on Evolutionary Computation, vol. 26, no. 6, pp. 1365–1379, 2022.
@article{Rehbach2022b,
title = {Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm},
author = {Frederik Rehbach and Martin Zaefferer and Andreas Fischbach and Gunter Rudolph and Thomas Bartz-Beielstein},
doi = {10.1109/tevc.2022.3163843},
year = {2022},
date = {2022-12-01},
journal = {IEEE Transactions on Evolutionary Computation},
volume = {26},
number = {6},
pages = {1365–1379},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Zhang, Yingqian; Catshoek, Tom; Vos, Daniël; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, André; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; López-Ibáñez, Manuel; Irurozki, Ekhine
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems Journal Article
In: arXiv, 2022.
@article{Bliek2022a,
title = {The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems},
author = {Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Yingqian Zhang and Tom Catshoek and Daniël Vos and Sicco Verwer and Fynn Schmitt-Ulms and André Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel López-Ibáñez and Ekhine Irurozki},
year = {2022},
date = {2022-01-01},
journal = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
Stork, Jörg; Zaefferer, Martin; Eisler, Nils; Tichelmann, Patrick; Bartz-Beielstein, Thomas; Eiben, A. E.
Behavior-based Neuroevolutionary Training in Reinforcement Learning Proceedings Article
In: Chicano, Francisco (Ed.): GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1753–1761, ACM, Lille, France, 2021.
@inproceedings{Stork2021a,
title = {Behavior-based Neuroevolutionary Training in Reinforcement Learning},
author = {Jörg Stork and Martin Zaefferer and Nils Eisler and Patrick Tichelmann and Thomas Bartz-Beielstein and A. E. Eiben},
editor = {Francisco Chicano},
year = {2021},
date = {2021-07-01},
booktitle = {GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {1753–1761},
publisher = {ACM},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bartz, Eva; Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization Journal Article
In: 2021.
@article{Bartz2021a,
title = {Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization},
author = {Eva Bartz and Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},
year = {2021},
date = {2021-07-01},
abstract = {Machine learning algorithms such as random forests or xgboost are gaining more importance and are increasingly incorporated into production processes in order to enable comprehensive digitization and, if possible, automation of processes. Hyperparameters of these algorithms used have to be set appropriately, which can be referred to as hyperparameter tuning or optimization. Based on the concept of tunability, this article presents an overview of theoretical and practical results for popular machine learning algorithms. This overview is accompanied by an experimental analysis of 30 hyperparameters from six relevant machine learning algorithms. In particular, it provides (i) a survey of important hyperparameters, (ii) two parameter tuning studies, and (iii) one extensive global parameter tuning study, as well as (iv) a new way, based on consensus ranking, to analyze results from multiple algorithms. The R package mlr is used as a uniform interface to the machine learning models. The R package SPOT is used to perform the actual tuning (optimization). All additional code is provided together with this paper.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Machine learning algorithms such as random forests or xgboost are gaining more importance and are increasingly incorporated into production processes in order to enable comprehensive digitization and, if possible, automation of processes. Hyperparameters of these algorithms used have to be set appropriately, which can be referred to as hyperparameter tuning or optimization. Based on the concept of tunability, this article presents an overview of theoretical and practical results for popular machine learning algorithms. This overview is accompanied by an experimental analysis of 30 hyperparameters from six relevant machine learning algorithms. In particular, it provides (i) a survey of important hyperparameters, (ii) two parameter tuning studies, and (iii) one extensive global parameter tuning study, as well as (iv) a new way, based on consensus ranking, to analyze results from multiple algorithms. The R package mlr is used as a uniform interface to the machine learning models. The R package SPOT is used to perform the actual tuning (optimization). All additional code is provided together with this paper.
Gentile, Lorenzo; Morales, Elisa; Zaefferer, Martin; Minisci, Edmondo; Quagliarella, Domenico; Bartz-Beielstein, Thomas; Tognaccini, Renato
High-Lift Devices Topology Robust Optimisation Using Machine Learning Assisted Optimisation Proceedings Article
In: Vasile, Massimiliano; Quagliarella, Domenico (Ed.): Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications, UQOP 2020, pp. 297–313, Springer, 2021.
@inproceedings{Gentile2021,
title = {High-Lift Devices Topology Robust Optimisation Using Machine Learning Assisted Optimisation},
author = {Lorenzo Gentile and Elisa Morales and Martin Zaefferer and Edmondo Minisci and Domenico Quagliarella and Thomas Bartz-Beielstein and Renato Tognaccini},
editor = {Massimiliano Vasile and Domenico Quagliarella},
doi = {10.1007/978-3-030-80542-5_18},
year = {2021},
date = {2021-01-01},
booktitle = {Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications, UQOP 2020},
volume = {8},
pages = {297–313},
publisher = {Springer},
series = {Space Technology Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bartz-Beielstein, Thomas; Dröscher, Marcel; Gür, Alpar; Hinterleitner, Alexander; Lawton, Tom; Mersmann, Olaf; Peeva, Dessislava; Reese, Lennard; Rehbach, Nicolas; Rehbach, Frederik; Sen, Amrita; Subbotin, Aleksandr; Zaefferer, Martin
Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic Proceedings Article
In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 728–735, IEEE 2021.
@inproceedings{bartz2021o,
title = {Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic},
author = {Thomas Bartz-Beielstein and Marcel Dröscher and Alpar Gür and Alexander Hinterleitner and Tom Lawton and Olaf Mersmann and Dessislava Peeva and Lennard Reese and Nicolas Rehbach and Frederik Rehbach and Amrita Sen and Aleksandr Subbotin and Martin Zaefferer},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE Congress on Evolutionary Computation (CEC)},
pages = {728–735},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bartz-Beielstein, Thomas; Dröscher, Marcel; Gür, Alpar; Hinterleitner, Alexander; Mersmann, Olaf; Peeva, Dessislava; Reese, Lennard; Rehbach, Nicolas; Rehbach, Frederik; Sen, Amrita; Subbotin, Aleksandr; Zaefferer, Martin
Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis Proceedings Article
In: Chicano, Francisco (Ed.): GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 293–294, ACM, Lille, France, 2021.
@inproceedings{Bartz-Beielstein2020f,
title = {Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis},
author = {Thomas Bartz-Beielstein and Marcel Dröscher and Alpar Gür and Alexander Hinterleitner and Olaf Mersmann and Dessislava Peeva and Lennard Reese and Nicolas Rehbach and Frederik Rehbach and Amrita Sen and Aleksandr Subbotin and Martin Zaefferer},
editor = {Francisco Chicano},
year = {2021},
date = {2021-07-01},
booktitle = {GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {293–294},
publisher = {ACM},
address = {Lille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stork, Jörg; Wenzel, Philip; Landwein, Severin; Algorri, Maria-Elena; Zaefferer, Martin; Kusch, Wolfgang; Staubach, Martin; Bartz-Beielstein, Thomas; Köhn, Hartmut; Dejager, Hermann; Wolf, Christian
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs Journal Article
In: arXiv, 2021.
@article{Stork2021c,
title = {Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs},
author = {Jörg Stork and Philip Wenzel and Severin Landwein and Maria-Elena Algorri and Martin Zaefferer and Wolfgang Kusch and Martin Staubach and Thomas Bartz-Beielstein and Hartmut Köhn and Hermann Dejager and Christian Wolf},
year = {2021},
date = {2021-07-01},
journal = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
Bartz-Beielstein, Thomas; Zaefferer, Martin
Big Data is often just Bad Data Proceedings Article
In: DigitalXchange, Gummersbach, 2020, (Online unter: urlhttps://www.youtube.com/watch?v=VSKaw69lF5k).
@inproceedings{Bartz2020b,
title = {Big Data is often just Bad Data},
author = {Thomas Bartz-Beielstein and Martin Zaefferer},
year = {2020},
date = {2020-01-01},
booktitle = {DigitalXchange},
address = {Gummersbach},
note = {Online unter: urlhttps://www.youtube.com/watch?v=VSKaw69lF5k},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zaefferer, Martin; Rehbach, Frederik
Continuous Optimization Benchmarks by Simulation Proceedings Article
In: Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, pp. 273–286, Springer, 2020.
@inproceedings{Zaefferer2020b,
title = {Continuous Optimization Benchmarks by Simulation},
author = {Martin Zaefferer and Frederik Rehbach},
year = {2020},
date = {2020-01-01},
booktitle = {Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference},
volume = {12269},
pages = {273–286},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rehbach, Frederik; Zaefferer, Martin; Naujoks, Boris; Bartz-Beielstein, Thomas
Expected improvement versus predicted value in surrogate-based optimization Proceedings Article
In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 868–876, ACM, Cancun, Mexico, 2020.
@inproceedings{Rehbach2020a,
title = {Expected improvement versus predicted value in surrogate-based optimization},
author = {Frederik Rehbach and Martin Zaefferer and Boris Naujoks and Thomas Bartz-Beielstein},
doi = {10.1145/3377930.3389816},
year = {2020},
date = {2020-07-01},
booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
pages = {868–876},
publisher = {ACM},
address = {Cancun, Mexico},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bartz, Eva; Zaefferer, Martin; Katagiri, Takeshi; Bartz-Beielstein, Thomas
Seillose, lineare Aufzüge und Künstliche Intelligenz Journal Article
In: Transforming Cities, vol. 2020, no. 2, pp. 12–14, 2020.
@article{Bartz2020a,
title = {Seillose, lineare Aufzüge und Künstliche Intelligenz},
author = {Eva Bartz and Martin Zaefferer and Takeshi Katagiri and Thomas Bartz-Beielstein},
year = {2020},
date = {2020-06-01},
journal = {Transforming Cities},
volume = {2020},
number = {2},
pages = {12–14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gentile, Lorenzo; Bartz-Beielstein, Thomas; Zaefferer, Martin
Sequential Parameter Optimization for Mixed-Discrete Problems Book Section
In: Optimization Under Uncertainty with Applications to Aerospace Engineering, pp. 333–355, Springer International Publishing, 2020.
@incollection{Gentile2020,
title = {Sequential Parameter Optimization for Mixed-Discrete Problems},
author = {Lorenzo Gentile and Thomas Bartz-Beielstein and Martin Zaefferer},
doi = {10.1007/978-3-030-60166-9_10},
year = {2020},
date = {2020-09-01},
booktitle = {Optimization Under Uncertainty with Applications to Aerospace Engineering},
pages = {333–355},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Peetz, Tom; Vogt, Sebastian; Zaefferer, Martin; Bartz-Beielstein, Thomas
Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools Journal Article
In: arXiv, 2020, (arXiv:2009.01696).
@article{Peetz2020a,
title = {Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools},
author = {Tom Peetz and Sebastian Vogt and Martin Zaefferer and Thomas Bartz-Beielstein},
year = {2020},
date = {2020-09-03},
journal = {arXiv},
abstract = {Testing new, innovative technologies is a crucial task for safety and acceptance. But how can new systems be tested if no historical real-world data exist? Simulation provides an answer to this important question. Classical simulation tools such as event-based simulation are well accepted. But most of these established simulation models require the specification of many parameters. Furthermore, simulation runs, e.g., CFD simulations, are very time consuming. Generative Adversarial Networks (GANs) are powerful tools for generating new data for a variety of tasks. Currently, their most frequent application domain is image generation. This article investigates the applicability of GANs for imitating simulations. We are comparing the simulation output of a technical system with the output of a GAN. To exemplify this approach, a well-known multi-car elevator system simulator was chosen. Our study demonstrates the feasibility of this approach. It also discusses pitfalls and technical problems that occurred during the implementation. Although we were able to show that in principle, GANs can be used as substitutes for expensive simulation runs, we also show that they cannot be used "out of the box". Fine tuning is needed. We present a proof-of-concept, which can serve as a starting point for further research.},
note = {arXiv:2009.01696},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Testing new, innovative technologies is a crucial task for safety and acceptance. But how can new systems be tested if no historical real-world data exist? Simulation provides an answer to this important question. Classical simulation tools such as event-based simulation are well accepted. But most of these established simulation models require the specification of many parameters. Furthermore, simulation runs, e.g., CFD simulations, are very time consuming. Generative Adversarial Networks (GANs) are powerful tools for generating new data for a variety of tasks. Currently, their most frequent application domain is image generation. This article investigates the applicability of GANs for imitating simulations. We are comparing the simulation output of a technical system with the output of a GAN. To exemplify this approach, a well-known multi-car elevator system simulator was chosen. Our study demonstrates the feasibility of this approach. It also discusses pitfalls and technical problems that occurred during the implementation. Although we were able to show that in principle, GANs can be used as substitutes for expensive simulation runs, we also show that they cannot be used "out of the box". Fine tuning is needed. We present a proof-of-concept, which can serve as a starting point for further research.
Bartz-Beielstein, Thomas; Zaefferer, Martin
Trinkwassersicherheit mit Predictive Analytics Proceedings Article
In: KI-basierte Technologien in der Wasserversorgung, DVGW Kongress, Bonn, 2020.
@inproceedings{Bartz2020c,
title = {Trinkwassersicherheit mit Predictive Analytics},
author = {Thomas Bartz-Beielstein and Martin Zaefferer},
year = {2020},
date = {2020-01-01},
booktitle = {KI-basierte Technologien in der Wasserversorgung},
publisher = {DVGW Kongress},
address = {Bonn},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stork, Jörg; Zaefferer, Martin; Bartz-Beielstein, Thomas; Eiben, A. E.
Understanding the Behavior of Reinforcement Learning Agents Journal Article
In: EasyChair Preprint, no. 3342, 2020.
@article{Stork2020a,
title = {Understanding the Behavior of Reinforcement Learning Agents},
author = {Jörg Stork and Martin Zaefferer and Thomas Bartz-Beielstein and A. E. Eiben},
year = {2020},
date = {2020-01-01},
journal = {EasyChair Preprint},
number = {3342},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stork, Jörg; Zaefferer, Martin; Bartz-Beielstein, Thomas; Eiben, A. E.
Understanding the Behavior of~Reinforcement Learning Agents Proceedings Article
In: BIOMA 2020: Bioinspired Optimization Methods and Their Applications, pp. 148–160, Springer, 2020.
@inproceedings{Stork2020b,
title = {Understanding the Behavior of~Reinforcement Learning Agents},
author = {Jörg Stork and Martin Zaefferer and Thomas Bartz-Beielstein and A. E. Eiben},
doi = {10.1007/978-3-030-63710-1_12},
year = {2020},
date = {2020-01-01},
booktitle = {BIOMA 2020: Bioinspired Optimization Methods and Their Applications},
volume = {12438},
pages = {148–160},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2019
Zaefferer, Martin; Bartz-Beielstein, Thomas; Rudolph, Günter
An Empirical Approach For Probing the Definiteness of Kernels Journal Article
In: Soft Computing, vol. 23, no. 21, pp. 10939–10952, 2019.
@article{Zaefferer2014e,
title = {An Empirical Approach For Probing the Definiteness of Kernels},
author = {Martin Zaefferer and Thomas Bartz-Beielstein and Günter Rudolph},
doi = {10.1007/s00500-018-3648-1},
year = {2019},
date = {2019-11-01},
journal = {Soft Computing},
volume = {23},
number = {21},
pages = {10939–10952},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stork, Jörg; Zaefferer, Martin; Bartz-Beielstein, Thomas
Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels Proceedings Article
In: Applications of Evolutionary Computation, pp. 504–519, Springer, Leipzig, Germany, 2019.
@inproceedings{Stork2019c,
title = {Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels},
author = {Jörg Stork and Martin Zaefferer and Thomas Bartz-Beielstein},
doi = {10.1007/978-3-030-16692-2_34},
year = {2019},
date = {2019-01-01},
booktitle = {Applications of Evolutionary Computation},
volume = {11454},
pages = {504–519},
publisher = {Springer},
address = {Leipzig, Germany},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stork, Jörg; Friese, Martina; Zaefferer, Martin; Bartz-Beielstein, Thomas; Fischbach, Andreas; Breiderhoff, Beate; Naujoks, Boris; Tušar, Tea
Open Issues in Surrogate-Assisted Optimization Book Section
In: High-Performance Simulation-Based Optimization, pp. 225–244, Springer International Publishing, 2019.
@incollection{Stork2019b,
title = {Open Issues in Surrogate-Assisted Optimization},
author = {Jörg Stork and Martina Friese and Martin Zaefferer and Thomas Bartz-Beielstein and Andreas Fischbach and Beate Breiderhoff and Boris Naujoks and Tea Tušar},
doi = {10.1007/978-3-030-18764-4_10},
year = {2019},
date = {2019-06-01},
booktitle = {High-Performance Simulation-Based Optimization},
pages = {225–244},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Hagg, Alexander; Zaefferer, Martin; Stork, Jörg; Gaier, Adam
Prediction of Neural Network Performance by Phenotypic Modeling Proceedings Article
In: López-Ibáñez, Manuel (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference Companion - GECCO'19, pp. 1576–1582, ACM, Prague, Czech Republic, 2019, ISBN: 978-1-4503-6748-6.
@inproceedings{Hagg2019a,
title = {Prediction of Neural Network Performance by Phenotypic Modeling},
author = {Alexander Hagg and Martin Zaefferer and Jörg Stork and Adam Gaier},
editor = {Manuel López-Ibáñez},
url = {http://doi.acm.org/10.1145/3319619.3326815},
doi = {10.1145/3319619.3326815},
isbn = {978-1-4503-6748-6},
year = {2019},
date = {2019-07-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion - GECCO'19},
pages = {1576–1582},
publisher = {ACM},
address = {Prague, Czech Republic},
series = {GECCO '19},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stork, Jörg; Zaefferer, Martin; Bartz-Beielstein, Thomas; Eiben, A. E.
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning Proceedings Article
In: López-Ibáñez, Manuel (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'19, pp. 934–942, ACM, Prague, Czech Republic, 2019, ISBN: 978-1-4503-6111-8.
@inproceedings{Stork2019a,
title = {Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning},
author = {Jörg Stork and Martin Zaefferer and Thomas Bartz-Beielstein and A. E. Eiben},
editor = {Manuel López-Ibáñez},
url = {http://doi.acm.org/10.1145/3321707.3321829},
doi = {10.1145/3321707.3321829},
isbn = {978-1-4503-6111-8},
year = {2019},
date = {2019-07-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'19},
pages = {934–942},
publisher = {ACM},
address = {Prague, Czech Republic},
series = {GECCO '19},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Horn, Daniel; Stork, Jörg; Schüßler, Nils-Jannik; Zaefferer, Martin
Surrogates for Hierarchical Search Spaces: The Wedge-kernel and an Automated Analysis Proceedings Article
In: López-Ibáñez, Manuel (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'19, pp. 916–924, ACM, Prague, Czech Republic, 2019, ISBN: 978-1-4503-6111-8.
@inproceedings{Horn2019a,
title = {Surrogates for Hierarchical Search Spaces: The Wedge-kernel and an Automated Analysis},
author = {Daniel Horn and Jörg Stork and Nils-Jannik Schüßler and Martin Zaefferer},
editor = {Manuel López-Ibáñez},
url = {http://doi.acm.org/10.1145/3321707.3321765},
doi = {10.1145/3321707.3321765},
isbn = {978-1-4503-6111-8},
year = {2019},
date = {2019-07-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'19},
pages = {916–924},
publisher = {ACM},
address = {Prague, Czech Republic},
series = {GECCO '19},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Chugh, Tinkle; Rahat, Alma; Volz, Vanessa; Zaefferer, Martin
Towards Better Integration of Surrogate Models and Optimizers Book Section
In: High-Performance Simulation-Based Optimization, pp. 137–163, Springer International Publishing, 2019.
@incollection{Chugh2019a,
title = {Towards Better Integration of Surrogate Models and Optimizers},
author = {Tinkle Chugh and Alma Rahat and Vanessa Volz and Martin Zaefferer},
doi = {10.1007/978-3-030-18764-4_7},
year = {2019},
date = {2019-06-01},
booktitle = {High-Performance Simulation-Based Optimization},
pages = {137–163},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2018
Zaefferer, Martin; Horn, Daniel
A First Analysis of Kernels for Kriging-Based Optimization in Hierarchical Search Spaces Proceedings Article
In: Auger, Anne; Fonseca, Carlos M.; Lourenço, Nuno; Machado, Penousal; Paquete, Luís; Whitley, Darrell (Ed.): Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, pp. 399–410, Springer, Coimbra, Portugal, 2018.
@inproceedings{Zaefferer2018a,
title = {A First Analysis of Kernels for Kriging-Based Optimization in Hierarchical Search Spaces},
author = {Martin Zaefferer and Daniel Horn},
editor = {Anne Auger and Carlos M. Fonseca and Nuno Lourenço and Penousal Machado and Luís Paquete and Darrell Whitley},
doi = {10.1007/978-3-319-99259-4_32},
year = {2018},
date = {2018-09-01},
booktitle = {Parallel Problem Solving from Nature – PPSN XV: 15th International Conference},
volume = {11102},
pages = {399–410},
publisher = {Springer},
address = {Coimbra, Portugal},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rehbach, Frederik; Zaefferer, Martin; Stork, Jörg; Bartz-Beielstein, Thomas
Comparison of parallel surrogate-assisted optimization approaches Proceedings Article
In: Aguirre, Hernan (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'18, pp. 1348–1355, ACM, Kyoto, Japan, 2018.
@inproceedings{Rehbach2017a,
title = {Comparison of parallel surrogate-assisted optimization approaches},
author = {Frederik Rehbach and Martin Zaefferer and Jörg Stork and Thomas Bartz-Beielstein},
editor = {Hernan Aguirre},
doi = {10.1145/3205455.3205587},
year = {2018},
date = {2018-07-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'18},
pages = {1348–1355},
publisher = {ACM},
address = {Kyoto, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stork, Jörg; Zaefferer, Martin; Bartz-Beielstein, Thomas
Distance-based Kernels for Surrogate Model-based Neuroevolution Journal Article
In: arXiv, 2018, (Accepted to the Developmental Neural Networks Workshop at the Parallel Problem Solving from Nature 2018 (PPSN XV) conference. arXiv:1807.07839).
@article{Stork2018a,
title = {Distance-based Kernels for Surrogate Model-based Neuroevolution},
author = {Jörg Stork and Martin Zaefferer and Thomas Bartz-Beielstein},
year = {2018},
date = {2018-07-01},
journal = {arXiv},
note = {Accepted to the Developmental Neural Networks Workshop at the Parallel Problem Solving from Nature 2018 (PPSN XV) conference. arXiv:1807.07839},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zaefferer, Martin; Stork, Jörg; Flasch, Oliver; Bartz-Beielstein, Thomas
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming Proceedings Article
In: Auger, Anne; Fonseca, Carlos M.; Lourenço, Nuno; Machado, Penousal; Paquete, Luís; Whitley, Darrell (Ed.): Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, pp. 220–231, Springer, Coimbra, Portugal, 2018.
@inproceedings{Zaefferer2018b,
title = {Linear Combination of Distance Measures for Surrogate Models in Genetic Programming},
author = {Martin Zaefferer and Jörg Stork and Oliver Flasch and Thomas Bartz-Beielstein},
editor = {Anne Auger and Carlos M. Fonseca and Nuno Lourenço and Penousal Machado and Luís Paquete and Darrell Whitley},
doi = {10.1007/978-3-319-99259-4_18},
year = {2018},
date = {2018-09-01},
booktitle = {Parallel Problem Solving from Nature – PPSN XV: 15th International Conference},
volume = {11102},
pages = {220–231},
publisher = {Springer},
address = {Coimbra, Portugal},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bartz-Beielstein, Thomas; Zaefferer, Martin; Pham, Quoc Cuong
Optimization via multimodel simulation Journal Article
In: Structural and Multidisciplinary Optimization, vol. 58, no. 3, pp. 919–933, 2018.
@article{Bartz-Beielstein2018a,
title = {Optimization via multimodel simulation},
author = {Thomas Bartz-Beielstein and Martin Zaefferer and Quoc Cuong Pham},
doi = {10.1007/s00158-018-1934-2},
year = {2018},
date = {2018-02-01},
journal = {Structural and Multidisciplinary Optimization},
volume = {58},
number = {3},
pages = {919–933},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gentile, Lorenzo; Zaefferer, Martin; Giugliano, Dario; Chen, Haofeng; Bartz-Beielstein, Thomas
Surrogate assisted optimization of particle reinforced metal matrix composites Proceedings Article
In: Aguirre, Hernan (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'18, pp. 1238–1245, ACM, Kyoto, Japan, 2018.
@inproceedings{Gentile2018b,
title = {Surrogate assisted optimization of particle reinforced metal matrix composites},
author = {Lorenzo Gentile and Martin Zaefferer and Dario Giugliano and Haofeng Chen and Thomas Bartz-Beielstein},
editor = {Hernan Aguirre},
doi = {10.1145/3205455.3205574},
year = {2018},
date = {2018-07-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference - GECCO'18},
pages = {1238–1245},
publisher = {ACM},
address = {Kyoto, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zaefferer, Martin
Surrogate Models for Discrete Optimization Problems PhD Thesis
Technische Universität Dortmund, 2018, (urlhttp://dx.doi.org/10.17877/DE290R-19857).
@phdthesis{Zaefferer2018c,
title = {Surrogate Models for Discrete Optimization Problems},
author = {Martin Zaefferer},
doi = {10.17877/DE290R-19857},
year = {2018},
date = {2018-12-01},
school = {Technische Universität Dortmund},
note = {urlhttp://dx.doi.org/10.17877/DE290R-19857},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
2017
Bartz-Beielstein, Thomas; Zaefferer, Martin; Rehbach, Frederik
In a Nutshell – The Sequential Parameter Optimization Toolbox Journal Article
In: arXiv, 2017, (arXiv:1712.04076v2).
@article{Bartz-Beielstein2017p,
title = {In a Nutshell – The Sequential Parameter Optimization Toolbox},
author = {Thomas Bartz-Beielstein and Martin Zaefferer and Frederik Rehbach},
year = {2017},
date = {2017-12-01},
journal = {arXiv},
note = {arXiv:1712.04076v2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Jung, Christian; Zaefferer, Martin; Bartz-Beielstein, Thomas; Rudolph, Günter
Metamodel-based optimization of hot rolling processes in the metal industry Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 90, no. 1, pp. 421–435, 2017.
@article{Jung2016,
title = {Metamodel-based optimization of hot rolling processes in the metal industry},
author = {Christian Jung and Martin Zaefferer and Thomas Bartz-Beielstein and Günter Rudolph},
url = {http://dx.doi.org/10.1007/s00170-016-9386-6},
doi = {10.1007/s00170-016-9386-6},
year = {2017},
date = {2017-04-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {90},
number = {1},
pages = {421–435},
publisher = {Springer Nature},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bartz-Beielstein, Thomas; Zaefferer, Martin
Model-based Methods for Continuous and Discrete Global Optimization Journal Article
In: Applied Soft Computing, vol. 55, pp. 154 - 167, 2017.
@article{Bartz-Beielstein2016n,
title = {Model-based Methods for Continuous and Discrete Global Optimization},
author = {Thomas Bartz-Beielstein and Martin Zaefferer},
url = {https://doi.org/10.1016%2Fj.asoc.2017.01.039},
doi = {10.1016/j.asoc.2017.01.039},
year = {2017},
date = {2017-02-01},
journal = {Applied Soft Computing},
volume = {55},
pages = {154 - 167},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zaefferer, Martin; Fischbach, Andreas; Naujoks, Boris; Bartz-Beielstein, Thomas
Simulation Based Test Functions for Optimization Algorithms Proceedings Article
In: Proceedings of the Genetic and Evolutionary Computation Conference 2017, pp. 8, ACM, Berlin, Germany, 2017.
@inproceedings{Zaefferer2017a,
title = {Simulation Based Test Functions for Optimization Algorithms},
author = {Martin Zaefferer and Andreas Fischbach and Boris Naujoks and Thomas Bartz-Beielstein},
doi = {http://dx.doi.org/10.1145/3071178.3071190},
year = {2017},
date = {2017-07-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2017},
pages = {8},
publisher = {ACM},
address = {Berlin, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zaefferer, Martin
Surrogate Model Based Optimization of the Substrate Feed Mixture of a Biogas Plant Proceedings Article
In: 15th Workshop on Quality Improvement Methods, Dortmund, Germany, 2017.
@inproceedings{Zaefferer2017e,
title = {Surrogate Model Based Optimization of the Substrate Feed Mixture of a Biogas Plant},
author = {Martin Zaefferer},
year = {2017},
date = {2017-01-01},
booktitle = {15th Workshop on Quality Improvement Methods},
address = {Dortmund, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stork, Jörg; Zaefferer, Martin; Fischbach, Andreas; Bartz-Beielstein, Thomas
Surrogate-Assisted Learning of Neural Networks Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke; Mikut, Ralf (Ed.): Proceedings 27. Workshop Computational Intelligence, pp. 223–235, KIT Scientific Publishing, Dortmund, Germany, 2017.
@inproceedings{Stork2017c,
title = {Surrogate-Assisted Learning of Neural Networks},
author = {Jörg Stork and Martin Zaefferer and Andreas Fischbach and Thomas Bartz-Beielstein},
editor = {Frank Hoffmann and Eyke Hüllermeier and Ralf Mikut},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings 27. Workshop Computational Intelligence},
pages = {223–235},
publisher = {KIT Scientific Publishing},
address = {Dortmund, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bartz-Beielstein, Thomas; Zaefferer, Martin; Stork, Jörg; Krey, Sebastian
The Revised Sequential Parameter Optimization Toolbox Miscellaneous
useR!2017 Talk, 2017.
@misc{Bartz-Beielstein2017i,
title = {The Revised Sequential Parameter Optimization Toolbox},
author = {Thomas Bartz-Beielstein and Martin Zaefferer and Jörg Stork and Sebastian Krey},
year = {2017},
date = {2017-01-01},
howpublished = {useR!2017 Talk},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
2016
Chandrasekaran, Sowmya; Zaefferer, Martin; Moritz, Steffen; Stork, Jörg; Friese, Martina; Fischbach, Andreas; Bartz-Beielstein, Thomas
Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke; Mikut, Ralf (Ed.): Proceedings 26. Workshop Computational Intelligence, pp. 77–95, KIT Scientific Publishing, Dortmund, Germany, 2016.
@inproceedings{Chandrasekaran2016,
title = {Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs},
author = {Sowmya Chandrasekaran and Martin Zaefferer and Steffen Moritz and Jörg Stork and Martina Friese and Andreas Fischbach and Thomas Bartz-Beielstein},
editor = {Frank Hoffmann and Eyke Hüllermeier and Ralf Mikut},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings 26. Workshop Computational Intelligence},
pages = {77–95},
publisher = {KIT Scientific Publishing},
address = {Dortmund, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zaefferer, Martin; Bartz-Beielstein, Thomas
Efficient Global Optimization with Indefinite Kernels Proceedings Article
In: Handl, Julia; Hart, Emma; Lewis, Peter R.; López-Ibáñez, Manuel; Ochoa, Gabriela; Paechter, Ben (Ed.): Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, pp. 69–79, Springer, Edinburgh, UK, 2016.
@inproceedings{Zaefferer2016b,
title = {Efficient Global Optimization with Indefinite Kernels},
author = {Martin Zaefferer and Thomas Bartz-Beielstein},
editor = {Julia Handl and Emma Hart and Peter R. Lewis and Manuel López-Ibáñez and Gabriela Ochoa and Ben Paechter},
year = {2016},
date = {2016-01-01},
booktitle = {Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference},
volume = {9921},
pages = {69–79},
publisher = {Springer},
address = {Edinburgh, UK},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
86 entries « ‹ 1 of 2
› »