Published in dissertation, 2021
Hagg, A. (2021). Discovering the preference hypervolume: an interactive model for real world computational co-creativity (Doctoral dissertation, Leiden University).
Recommended citation: Hagg, A. (2021). Discovering the preference hypervolume: an interactive model for real world computational co-creativity (Doctoral dissertation, Leiden University).
Published in In the proceedings of Metaheuristics for Finding Multiple Solutions, 2021
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Recommended citation: Alexander Hagg, "Phenotypic Niching Using Quality Diversity Algorithms." In the proceedings of Metaheuristics for Finding Multiple Solutions, 2021.
Published in In the proceedings of Proceedings of the Genetic and Evolutionary Computation Conference, 2021
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Recommended citation: Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas B{\"{a}}ck, "Expressivity of parameterized and data-driven representations in quality diversity search." In the proceedings of Proceedings of the Genetic and Evolutionary Computation Conference, 2021.
Published in spinfortec 2020 digital, 2020
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Recommended citation: Alexander Hagg, Alexander Asteroth, Mark Pfeiffer, Fabian Hammes, Daniel Link, "Einsatzm"oglichkeiten und Transfer von K"unstlicher Intelligenz im inter-nationalen Spitzensport–zwischen Small und Big Data." spinfortec 2020 digital, 2020.
Published in spinfortec 2020 digital, 2020
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Recommended citation: Fabian Hammes, Daniel Link, Martin Lames, Alexander Hagg, Alexander Asteroth, Mark Pfeiffer, "Einsatz von K"unstlicher Intelligenz im internationalen Spitzensport–Eine Erhebung des Status Quo." spinfortec 2020 digital, 2020.
Published in Parallel Problem Solving From Nature (PPSN), 2020
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Recommended citation: Alexander Hagg, Dominik Wilde, Alexander Asteroth, Thomas B{\"{a}}ck, "Designing Air Flow with Surrogate-assisted Phenotypic Niching." Parallel Problem Solving From Nature (PPSN), 2020.
Published in In the proceedings of BIOMA 2020, 2020
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Recommended citation: Alexander Hagg, Mike Preuss, Alexander Asteroth, Thomas B{\"{a}}ck, "An Analysis of Phenotypic Diversity in Multi-Solution Optimization." In the proceedings of BIOMA 2020, 2020.
Published in In the proceedings of ICCC, 2020
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Recommended citation: Alexander Hagg, Alexander Asteroth, Thomas B{\"{a}}ck, B Thomas, "A Deep Dive Into Exploring the Preference Hypervolume." In the proceedings of ICCC, 2020.
Published in Genetic and Evolutionary Computation Conference Companion (GECCO), 2019
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Recommended citation: Alexander Hagg, Martin Zaefferer, J{\"{o}}rg Stork, Adam Gaier, "Prediction of neural network performance by phenotypic modeling." Genetic and Evolutionary Computation Conference Companion (GECCO), 2019.
Published in GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019
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Recommended citation: Alexander Hagg, Alexander Asteroth, Thomas B{\"{a}}ck, "Modeling User Selection in Quality Diversity." GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019.
Published in In the proceedings of Parallel Problem Solving From Nature (PPSN), 2018
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Recommended citation: Alexander Hagg, Alexander Asteroth, Thomas B{\"{a}}ck, "Prototype Discovery using Quality-Diversity." In the proceedings of Parallel Problem Solving From Nature (PPSN), 2018.
Published in Soft Computing, 2017
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Recommended citation: Helge Spieker, Alexander Hagg, Adam Gaier, Stefanie Meilinger, Alexander Asteroth, "Multi-stage evolution of single-and multi-objective MCLP." Soft Computing, 2017.
Published in In the proceedings of Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
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Recommended citation: Alexander Hagg, "Hierarchical surrogate modeling for illumination algorithms." In the proceedings of Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017.
Published in In the proceedings of GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference, 2017
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Recommended citation: A. Hagg, M. Mensing, A. Asteroth, "Evolving parsimonious networks by mixing activation functions." In the proceedings of GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference, 2017.
Published in RoboCup Symposium, 2016
This tutorial gives a summary on various representational aspects, discuss parametrization and their influence on the dynamics of genetic algorithms.
Recommended citation: Hagg, A., Hegger, F., & Plöger, P. G. (2016, June). On recognizing transparent objects in domestic environments using fusion of multiple sensor modalities. In Robot world cup (pp. 3-15). Springer, Cham. http://www.ais.uni-bonn.de/robocup.de/2016/papers/RoboCup_Symposium_2016_Hagg.pdf
Published in Soft Computing, 2016
We extend our previous work by including multi-objective optimization of multi-stage charging station placement, allowing us to not only optimize toward (weighted) demand location coverage, but also to include a second objective, taking into account traffic density.
Recommended citation: Spieker, H., Hagg, A., Gaier, A., Meilinger, S., & Asteroth, A. (2017). Multi-stage evolution of single-and multi-objective MCLP. Soft Computing, 21(17), 4859-4872. https://hspieker.de/files/Spieker_et_al._-_2017_-_Multi-stage_evolution_of_single-_and_multi-objective_MCLP.pdf
Published in In the proceedings of Robot world cup, 2016
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Recommended citation: Alexander Hagg, Frederik Hegger, P.G. Pl{\"{o}}ger, "On recognizing transparent objects in domestic environments using fusion of multiple sensor modalities." In the proceedings of Robot world cup, 2016.
Published in INISTA, 2015
This tutorial gives a summary on various representational aspects, discuss parametrization and their influence on the dynamics of genetic algorithms.
Recommended citation: Asteroth, A., & Hagg, A. (2015, September). How to successfully apply genetic algorithms in practice: Representation and parametrization. In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/iel7/7269504/7276713/07276778.pdf?casa_token=jVllPT3Be6UAAAAA:FRjJUT_SwKdSiyb2e5_nsxA79PTuhOH8KO2DTYCUYZ8ZbOlweYEDdrOJszGC2lMGXor606S7F0M
Published in INISTA, 2015
An evolutionary algorithm is used to evolve a strategy for multi-stage placement of charging stations for electrical cars. Both an incremental as well as a decremental placement decomposition are evaluated on this Maximum Covering Location Problem.
Recommended citation: Spieker, H., Hagg, A., Asteroth, A., Meilinger, S., Jacobs, V., & Oslislo, A. (2015, September). Successive evolution of charging station placement. In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA) (pp. 1-7). IEEE. https://ieeexplore.ieee.org/iel7/7269504/7276713/07276733.pdf?casa_token=SRdRjyPkVHoAAAAA:iyMpFLclx3g-XvPSKmXGUjFXtpr_EzW0NEpfkY0bdFxWlW-WE8TCkucJZUm-wVOG7zESeq_1okQ
Published in In the proceedings of 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015
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Recommended citation: Helge Spieker, Alexander Hagg, Alexander Asteroth, Stefanie Meilinger, Volker Jacobs, Alexander Oslislo, "Successive evolution of charging station placement." In the proceedings of 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015.
Published in In the proceedings of 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015
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Recommended citation: Alexander Asteroth, Alexander Hagg, "How to successfully apply genetic algorithms in practice: Representation and parametrization." In the proceedings of 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015.
Published in RoboCup, Jo~ao Pessoa, Brazil, 2014
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Recommended citation: Rhama Dwiputra, Matthias F{\"{u}}ller, Frederik Hegger, Sven Schneider, Nico Hochgeschwender, Iman Awaad, Jos{\'{e}} Loza, Alexey Ozhigov, Saugata Biswas, Niranjan Deshpande, "The b-it-bots Robo-Cup@ Home 2014 Team Description Paper." RoboCup, Jo~ao Pessoa, Brazil, 2014.