Category Archives: Uncategorized

Vortrag @ TH Nürnberg — Folien / Code / Daten

HTML Datei mit den Präsentationsfolien zu “Sensordatenfusion mit Kalman-Filtern”:

und zu “Tuning a Neural Network Model for Water Sensor Data”:

Code und Daten sind ebenso verfügbar:

“Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs”

Our team here in Gummersbach has recently released an early version of our work on detecting security risks in public drinking water reservoirs. This work is based on different sensors which are suitable to detect objects and sounds under water, and uses neural networks to detect anormal events as well as specific events. Feel free to have a look at this paper, which is available on arXiv (external link):

Talks at FHWS in Würzburg, 20.07.2021

The following documents are the slides (pdf and html) from my talks at FHWS in Würzburg.

Estimation of mixture distributions:

Tuning a reinforcement learning algorithm:

In addition, the code from the slides and the underlying experiments are also provided:

Interesting talks at GECCO 2021, by the IDEA Team

There have been som interesting talks by my colleagues and co-authors at GECCO 2021:

Thomas Bartz-Beielstein presented a keynote at the SAEOpt Workshop, talking about surrogate model-based tuning of deep learning algorithms:

Sowmya Chandrasekaran presented the GECCO industrial challenge:

Jörg Stork presented our paper on behavior-based neuroevolutionary training in reinforcement learning:

Updated – on CRAN: CEGO Version 2.4.2

A new version (2.4.2) of my R-package CEGO was recently uploaded to CRAN.

This update contained an important fix to one of the distance functions: distancePermutationSwap. As kindly reported by Manuel López-Ibáñez, that distance was previously computing the swap distance on the inverse of the provided permutations. This is now corrected. The original behavior can be reproduced via distancePermutationSwapInv.

This issue and the impact it can have on the results with CEGO in optimization runs is discussed in an article by Irurozki and López-Ibáñez, which also covers an interesting modeling approach based on Mallows models, see:

Ekhine Irurozki and Manuel López-Ibáñez. Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021. ACM Press, New York, NY, 2021. doi: 10.1145/3449639.3459366

For the updated CEGO Package see also (external link)

GECCO 2021 Industrial Challenge

The IDE+A team at TH Köln is hosting the ‘Industrial challenge’ at the GECCO 2021. The goal is to find optimal parameters for the BaBSim.Hospital simulator. Participants can publish their algorithms and the top 3 solutions will be honored with price money.

More Information:

Article Update: Model-based methods for continuous and discrete global optimization

Just a small update for the article mentioned in the last post: Model-based methods for continuous and discrete global optimization.

Until April 11, 2017, the following link will direct you to the final version of the article on ScienceDirect (free, without personal or institutional registration).

(external link:)