Introducing surrogate models

In my last post, where I wrote about sequential parameter optimization, I asked you if you did some assumptions about the underlying function when trying to find the optimal spot. (If you missed this and you’re interested in an easy introduction to sequential parameter optimization you might like to have a look here.)
But back to the assumptions. If you don’t want to do a random search, you definitely take some assumptions about the underlying function. That might be something simple, like ‘following the steepest path leads to the highest spot’ or more complex assumptions about the nature of the function, like ‘it might be polynomial’.
The former assumption can be used for a simple search strategy, the latter for building a surrogate model that hopefully resembles the function. Continue reading

Talking about my work

Bioma 2016 in Bled, Slovenia

Two weeks ago the 7th International Conference on Bioinspired Optimization Methods and their Applications took place in Bled, Slovenia. It’s a biennial conference so I was really lucky, that my work by than head reached a stage where I already had gained some positive and interesting results and was able to submit my paper just in time.
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Why now?

Yes why do I start this blog right now while already being working on my PhD for a while now?
Maybe it’s because I believe it’s on the verge of getting exciting right now. Anyhow, right now I’m for the first time now in the mood to talk about it.
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hello world!

With these words I’ll initiate my new blog where I’m (hopefully) will be logging my adventures as a PhD student 🙂