Position innerhalb des Seitenbaumes

Institut für Visualisierung und Interaktive Systeme
Team
Franziska Huth

Franziska Huth

Frau M.Sc.

Wiss. Mitarbeiterin
Institut für Visualisierung und Interaktive Systeme (VIS)
Virtual Reality und Augmented Reality

Kontakt

+49 711 685 88246
+49 711 685 78246
E-Mail
Visitenkarte (VCF)

Universitätsstraße 38
70569 Stuttgart
Deutschland
Raum: 1.438

Fachgebiet

Volunteered Geographic Information Research Programme

KONTEXT(VER)ÄNDERUNGEN IN SOCIAL-MEDIA-BEITRÄGEN

Publikationen

Publikationen:

2015
1. Doerr, Benjamin ; Doerr, Carola ; Huth, Franziska: From black-box complexity to designing new genetic algorithms. In: Theoretical Computer Science, Theoretical Computer Science. Bd. 567 (2015), S. 87–104
  - BibTeX
  - Link
  BibTeX
  @article{DOERR201587, abstract = {Black-box complexity theory recently produced several surprisingly fast black-box optimization algorithms. In this work, we exhibit one possible reason: These black-box algorithms often profit from solutions inferior to the previous-best. In contrast, evolutionary approaches guided by the “survival of the fittest” paradigm often ignore such solutions. We use this insight to design a new crossover-based genetic algorithm. It uses mutation with a higher-than-usual mutation probability to increase the exploration speed and crossover with the parent to repair losses incurred by the more aggressive mutation. A rigorous runtime analysis proves that our algorithm for many parameter settings is asymptotically faster on the OneMax test function class than all what is known for classic evolutionary algorithms. A fitness-dependent choice of the offspring population size provably reduces the expected runtime further to linear in the dimension. Our experimental analysis on several test function classes shows advantages already for small problem sizes and broad parameter ranges. Also, a simple self-adaptive choice of these parameters gives surprisingly good results.}, author = {Doerr, Benjamin and Doerr, Carola and Huth, Franziska}, doi = {https://doi.org/10.1016/j.tcs.2014.11.028}, issn = {0304-3975}, journal = {Theoretical Computer Science}, pages = {87-104}, title = {From black-box complexity to designing new genetic algorithms.}, url = {http://www.sciencedirect.com/science/article/pii/S0304397514009451}, volume = 567, year = 2015 }
  Link
  http://www.sciencedirect.com/science/article/pii/S0304397514009451
2014
1. Schmaltz, Christian ; Peter, Pascal ; Mainberger, Markus ; Huth, Franziska ; Weickert, Joachim ; Bruhn, Andrés: Understanding, Optimising, and Extending Data Compression with Anisotropic Diffusion. In: International Journal of Computer Vision, International Journal of Computer Vision. Bd. 108 (2014), Nr. 3, S. 222–240
  - BibTeX
  - Link
  BibTeX
  @article{journals/ijcv/SchmaltzPMEWB14, abstract = {Gali{\'{c}} et al. (Journal of Mathematical Imaging and Vision 31:255--269, 2008) have shown that compression based on edge-enhancing anisotropic diffusion (EED) can outperform the quality of JPEG for medium to high compression ratios when the interpolation points are chosen as vertices of an adaptive triangulation. However, the reasons for the good performance of EED remained unclear, and they could not outperform the more advanced JPEG 2000. The goals of the present paper are threefold: Firstly, we investigate the compression qualities of various partial differential equations. This sheds light on the favourable properties of EED in the context of image compression. Secondly, we demonstrate that it is even possible to beat the quality of JPEG 2000 with EED if one uses specific subdivisions on rectangles and several important optimisations. These amendments include improved entropy coding, brightness and diffusivity optimisation, and interpolation swapping. Thirdly, we demonstrate how to extend our approach to 3-D and shape data. Experiments on classical test images and 3-D medical data illustrate the high potential of our approach.}, author = {Schmaltz, Christian and Peter, Pascal and Mainberger, Markus and Huth, Franziska and Weickert, Joachim and Bruhn, Andrés}, doi = {10.1007/s11263-014-0702-z}, issn = {1573-1405}, journal = {International Journal of Computer Vision}, number = 3, pages = {222-240}, title = {Understanding, Optimising, and Extending Data Compression with Anisotropic Diffusion.}, url = {https://doi.org/10.1007/s11263-014-0702-z}, volume = 108, year = 2014 }
  Link
  https://doi.org/10.1007/s11263-014-0702-z
2013
1. Doerr, Benjamin ; Doerr, Carola ; Huth, Franziska: Lessons from the black-box: fast crossover-based genetic algorithms. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation. Amsterdam, The Netherlands : ACM, 2013. — Best Paper Award — ISBN 978-1-4503-1963-8, S. 781–788
  - BibTeX
  - Link
  BibTeX
  @inproceedings{Doerr:2013:LBF:2463372.2463480, abstract = {The recently active research area of black-box complexity revealed that for many optimization problems the best possible black-box optimization algorithm is significantly faster than all known evolutionary approaches. While it is not to be expected that a general-purpose heuristic competes with a problem-tailored algorithm, it still makes sense to look for the reasons for this discrepancy. In this work, we exhibit one possible reason---most optimal black-box algorithms profit also from solutions that are inferior to the previous-best one, whereas evolutionary approaches guided by the "survival of the fittest" paradigm often ignore such solutions. Trying to overcome this shortcoming, we design a simple genetic algorithm that first creates λ offspring from a single parent by mutation with a mutation probability that is k times larger than the usual one. From the best of these offspring (which often is worse than the parent) and the parent itself, we generate further offspring via a uniform crossover operator that takes bits from the winner offspring with probability 1/k only. A rigorous runtime analysis proves that our new algorithm for suitable parameter choices on the OneMax test function class is asymptotically faster (in terms of the number of fitness evaluations) than what has been shown for μ +, λ EAs. This is the first time that crossover is shown to give an advantage for the OneMax class that is larger than a constant factor. Using a fitness-dependent choice of k and λ, the optimization time can be reduced further to linear in n. Our experimental analysis on several test function classes shows advantages already for small problem sizes and broad parameter ranges. Also, a simple self-adaptive choice of these parameters gives surprisingly good results.}, acmid = {2463480}, address = {New York, NY, USA}, author = {Doerr, Benjamin and Doerr, Carola and Huth, Franziska}, booktitle = {Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation}, doi = {10.1145/2463372.2463480}, isbn = {978-1-4503-1963-8}, location = {Amsterdam, The Netherlands}, note = {Best Paper Award}, numpages = {8}, pages = {781-788}, publisher = {ACM}, series = {GECCO '13}, title = {Lessons from the black-box: fast crossover-based genetic algorithms.}, url = {http://doi.acm.org/10.1145/2463372.2463480}, year = 2013 }
  Link
  http://doi.acm.org/10.1145/2463372.2463480

Lehre

since 04/2021: Visualisierungskolloquium , University of Stuttgart, Germany
10/2019--02/2020: Theoretical and Methodological Foundations of Visual Computing, University of Stuttgart, Germany
04/2019--09/2019: Scientific Visualisation, University of Stuttgart, Germany

Social Media

ORCID

https://orcid.org/0000-0003-1393-5641

Franziska Huth

Kontakt

Fachgebiet

Publikationen

2015

BibTeX

Link

2014

BibTeX

Link

2013

BibTeX

Link

Lehre

Social Media

ORCID

Zielgruppe

Formalia

Services

Organisation

Franziska Huth

Kontakt

Fachgebiet

Publikationen

2015

BibTeX

Link

2014

BibTeX

Link

2013

BibTeX

Link

Lehre

Social Media

ORCID

So erreichen Sie uns

Zielgruppe

Formalia

Services

Organisation