WSJ: Fatal Tesla Crash in Texas Believed to Be Driverless

Two men died after a Tesla vehicle that authorities believe was operating without anyone in the driver’s seat crashed into a tree Saturday night north of Houston. One of the men was in the front passenger’s seat and the other was in the back seat of the Tesla, which was traveling at high speed along a curve before it hit a tree around 11:25 p.m. local time, Harris County Precinct 4 Constable Mark Herman said in an interview. More: Wall Street Article https://www.wsj.com/articles/fatal-tesla-crash-in-texas-believed-to-be-driverless-11618766363

Here is the link to the video: https://video-api.wsj.com/api-video/player/v3/iframe.html?guid=0A94412D-62E2-4BDC-B619-B3B51C6A4B84

EvoStar2021 conference goes online

EvoStar2021 conference goes online and will be held from the 7th to the 9th of April of 2021.
Our paper “Bayesian Networks for Mood Prediction Using Unobstrusive Ecological Momentary Assesments” (Margarita Alejandra Rebolledo Coy, A.E. Eiben and Thomas Bartz-Beielstein), see program overview, www.evostar.org/2021/programme/ that holds the links to all the sessions and  https://easychair.org/smart-program/Evo2021/ for the Full Programme and details. The opening session and the keynotes will be live-streamed in the SPECIES YouTube Channel:  www.youtube.com/c/SPECIESsociety

Check the website for additional information:
www.evostar.org/2021

Paper “Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic” accepted for oral presentation at the IEEE CEC 2021

Authors:
Thomas Bartz-Beielstein, Marcel Droescher, Alpar Guer, Alexander Hinterleitner, Tom Lawton, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Frederik Rehbach, Nicolas Rehbach, Amrita Sen, Aleksandr Subbotin and Martin Zaefferer

Abstract:
Hospitals and health-care institutions need to plan the resources required for
handling the increased load, i.e., beds and ventilators during the COVID-19
pandemic.
BaBSim.Hospital, an open-source tool for capacity planning based on discrete
event simulation, was developed over the last year to support doctors,
administrations, health authorities, and crisis teams in Germany.
To obtain reliable results, 29 simulation parameters such as durations and
probabilities must be specified. While reasonable default values were obtained
in detailed discussions with medical professionals, the parameters have to be
regularly and automatically optimized based on current data.

We aim to investigate how a set of parameters that is tailored to the German
health system can be transferred to other regions. Therefore, we will use data
from the UK. Our study demonstrates the flexibility of the discrete event
simulation approach. However, transferring the optimal German parameter settings
to the UK situation does not work—parameter ranges must be modified. The
adaptation has been shown to reduce simulation errors by nearly 70%.
The simulation-via-optimization approach is not restricted to health-care
institutions, it is applicable to many other real-world problems, e.g., the
development of new elevator systems to cover the last mile or simulation of
student flow in academic study periods.

Link to the simulator:
https://covid-resource-sim.th-koeln.de/app/babsim.hospitalvis

Link to the open-source software:
https://cran.r-project.org/package=babsim.hospital

Update: GECCO 2021 Industrial Challenge – Competition on Optimization of a Simulation Model for Capacity and Resource Planing Task for hospitals under special consideration of the COVID-19 Pandemic

Competition website:https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-2021-industrial-challenge-call-for-participation_82086.php

Highlights of the GECCO 2021 Industrial Challenge include:

  • Interesting Problem Domain: The impact of COVID-19 on the health system is ongoing and tools to help in capacity planning are more important than ever.
  • Real-world Problems: Test your algorithms and methods, directly on real and current data.
  • Easy Access: Easily Participate through our online platform, no installations required.
  • Fair Submission Assessment: Winners are determined automatically through our online portal, fully objectively, only based on the final result quality.
  • Publication Possibilities: We are able to accept 2-page submissions for the GECCO Companion; thus, publications are possible directly through competition participation.
  • Price money:  The best solution will receive 300€, the second place 200€ and the third place 100€

Simulation models are valuable tools for resource usage estimation and capacity planning. The simulator, BaBSim.Hospital, explicitly covers difficulties for hospitals caused by the COVID-19 pandemic. The simulator can handle many aspects of resource planning in hospitals, such as ICU beds, ventilators or personal, while taking into consideration several cohorts as age or current health status. 
The task represents an instance of an expensive, high-dimensional computer simulation-based optimization problem. The simulations will be executed through an interface and hosted on one of our servers (similar to our last year’s challenge).

Your goal is to find an optimal parameter configuration for the BaBSim.Hospital simulator with a very limited budget of objective function evaluations. The participants will be free to apply one or multiple optimization algorithms of their choice.

Like last year, we are able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late-Breaking Abstracts (LBAs) directly through competition participation!

2-Page Algorithm Description Submission Deadline: 2021-04-12 23:59
To be held as part of the 2021 Genetic and Evolutionary Computation Conference (GECCO 2021) organized by ACM SIGEVO (https://gecco-2021.sigevo.org).

CfP: Workshop on Evolutionary Algorithms for Problems with Uncertainty

The 4th Workshop on Evolutionary Algorithms for Problems with Uncertainty http://eapwu.ex.ac.uk/
To be held as part of The Genetic and Evolutionary Computation Conference (GECCO 2021)Lille, France, July 10-14 2021.
Please note that GECCO 2021 will be held as an online/virtual-only conference. All accepted papers will be required to be presented in the form of a pre-recorded talk.

Submission opening: February 11, 2021
Submission deadline: April 12, 2021
Reviews due: April 22, 2021Decisions due: April 26, 2021
Camera-ready Material: May 3, 2021
Author registration deadline: May 3, 2021

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Tuning algorithms for black-box optimization: State of the art and future perspectives will be available in May 2021

The publication “Tuning algorithms for black-box optimization: State of the art and future perspectives” by Thomas Bartz-Beielstein, Frederik Rehbach, and Margarita Rebolledo will be published as a contribution to the book Black Box Optimization, Machine Learning and No- Free Lunch Theorems. The book is edited by Panos Pardalos, Varvara Rasskazova, and Michael Vrahatis and will be number 170 in Springer Optimization and Its Applications series, see https://www.springer.com/de/book/9783030665142

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GECCO 2021: COMPETITION ON OPTIMIZATION OF A SIMULATION MODEL FOR A CAPACITY AND RESOURCE PLANNING TASK FOR HOSPITALS UNDER SPECIAL CONSIDERATION OF THE COVID-19 PANDEMIC

Simulation models are valuable tools for resource usage estimation and capacity planning. The simulator, BaBSim.Hospital, explicitly covers difficulties for hospitals caused by the COVID-19 pandemic. The simulator can handle many aspects of resource planning in hospitals, such as ICU beds, ventilators or personal, while taking into consideration several cohorts as age or current health status. 
The task represents an instance of an expensive, high-dimensional computer simulation-based optimization problem. The simulations will be executed through an interface and hosted on one of our servers (similar to our last year’s challenge).

Your goal is to find an optimal parameter configuration for the BaBSim.Hospital simulator with a very limited budget of objective function evaluations. The participants will be free to apply one or multiple optimization algorithms of their choice.

Continue reading →