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):
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) https://cran.r-project.org/package=CEGO.
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.
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:) https://authors.elsevier.com/a/1Ub295aecSVmv2