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What and Who
Title:Black-Box Optimization under Uncertainty
Speaker:Dr. Timo Kötzing
coming from:Hasso-Plattner-Institut
Speakers Bio:
Event Type:AG1 Mittagsseminar (others' work)
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:AG Audience
Date, Time and Location
Date:Friday, 10 June 2016
Duration:60 Minutes
Building:E1 4
Black-box optimization considers optimization of problems which are only known via access to an objective function measuring the quality a given search point. Typical generic solvers to such problems include local search, but also many nature-inspired search heuristics such as Simulated Annealing and Evolutionary Algorithms. There is a growing community of theoretical computer scientists working on explaining the principles which make these search heuristics successful. In particular, recent progress shows that many of the considered approaches perform very well for optimization under uncertainty. In this talk I will discuss uncertainty stemming from noisy objective functions, where certain algorithms are particularly robust to noise, while others fail. I will show what distinguishes the successful algorithms from the less successful and present the used techniques (which deal with the analysis of stochastic processes) in a manner accessible to a wider audience
Name(s):Christina Fries
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):
Christina Fries/AG1/MPII/DE, 05/19/2016 09:57 AM
Last modified:
halma/MPII/DE, 11/07/2018 04:52 PM
  • Christina Fries, 05/19/2016 10:00 AM -- Created document.