Alexander Hagg

Alexander Hagg

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publications

The b-it-bots Robo-Cup@ Home 2014 Team Description Paper

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.

How to successfully apply genetic algorithms in practice: Representation and parametrization

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.

Successive evolution of charging station placement

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.

Successive evolution of charging station placement

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

How to successfully apply genetic algorithms in practice: Representation and parametrization.

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

Multi-stage evolution of single-and multi-objective MCLP.

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

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities.

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

Evolving parsimonious networks by mixing activation functions

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.

Hierarchical surrogate modeling for illumination algorithms

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.

Prototype Discovery using Quality-Diversity

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.

Modeling User Selection in Quality Diversity

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.

Prediction of neural network performance by phenotypic modeling

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.

Expressivity of parameterized and data-driven representations in quality diversity search

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.

Phenotypic Niching Using Quality Diversity Algorithms

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.

talks

teaching

Theoretical Computer Science

Undergraduate course, Bonn-Rhein-Sieg University of Applied Sciences, CS, 2011

This tutorial was given to students that had difficulties understanding the concepts of the theoretical computer science.

Algebra and Number Theory

Undergraduate course, Bonn-Rhein-Sieg University of Applied Sciences, CS, 2012

Exercise that accompanied the lecture series on abstract algebra and number theory.

Autonomous Mobile Robots

Graduate course, Bonn-Rhein-Sieg University of Applied Sciences, CS, 2013

For six semesters I was a teaching assistant, leading the exercises on programming for autonomous mobile robots. Using the Robot Operating System, Python and C++, students implemented algorithms for such tasks as path finding in a simulated environment. Coming from various backgrounds, the course also offered the possibility to learn programming from scratch. A high intensity course for first semester students, including individual and group work.

Genetic Algorithms & Neuroevolution

Undergraduate course, Bonn-Rhein-Sieg University of Applied Sciences, CS, 2015

For 10 semesters I gave courses on genetic algorithms and neuroevolution, including lectures and exercises and practical projects. Students implemented GAs, various genetic operators, and learned how to analyze results. In the neurevolution course, students learned how to evolve neural networks. Evolutionary techniques and neural networks were the two corner stones of these courses that I fully built and managed myself.

Evolutionary Computation, Theory and Application

Graduate course, Bonn-Rhein-Sieg University of Applied Sciences, CS, 2016

Master’s course on evolutionary computation, consisting of genetic algorithms, neuroevolution, genetic programming and the like. Heavily application oriented.