Late last week, Dave Cohen, Pete Jeavons & I updated our paper on what we can learn about easy vs hard fitness landscapes by studying structure of gene-interaction networks (VCSP instances) that represent them: arxiv.org/abs/1907.01218

Now with many more pictures; prettier, too! ImageImageImageImage
I've already made a thread about the previous version of this work that was published in CP2019:

But if there is interest then I could provide some tweets on the new results and changes in this version.
The final version of our paper looking at the kinds of structure of gene-interaction networks that guarantee easy fitness landscapes is finally out: arxiv.org/abs/1907.01218…

It should appear in JAIR soon, and I think I'll do an updated thread on some of the new results then.
For those looking for the final home in Journal of Artificial Intelligence Research, it is now out.

Representing Fitness Landscapes by Valued Constraints to Understand the Complexity of Local Search: jair.org/index.php/jair…

I'm excited for all the bio follow-up work this opens!

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More from @kaznatcheev

28 Nov
Elimination of mandatory retirement at 65 is a factor leading to bad academic job market: sciencemag.org/careers/2018/1…

Solution: semi-retirement.

"[Having waited to semi-retire until] 74, I in essence removed 9 years from someone else’s career. I should have stepped aside sooner."
I'd be interested to see a graph of average age of retirement for tenured academics on the same plot as the average age of starting a tenured job over the last decades.

I should probably find Larson's paper and see if that sort of data is there.
The paper is here: pubsonline.informs.org/doi/abs/10.128…

Unfortunately, not very big on data. So it doesn't have the figure I want, but the closest is their simulation of number of years as faculty in the lower figure. Image
Read 4 tweets
5 Mar 19
Local peaks can't always be found quickly! Hard landscapes are subject to ultimate constraint on evolution: computation. Can hide winding paths.

genetics.org/content/early/…

My path to this paper has been very long: ~7 years in the making. It finally found its peak in @GeneticsGSA.
I'm glad to see this paper is popular with twitter. Thanks!

Since I don't have a SoundCloud to plug, I thought I'd make a tweetstorm summarizing the main results of this paper.

There is a lot to go through, so I apologize for the length of the thread. But I hope you enjoy!
Evolutionary constrains keep populations away from local optima (peaks) in fitness landscapes.

I introduce a division of constraints into 2 types: proximal & ultimate.

This is related to computer science distinction between algorithms & problems [1/n]: egtheory.wordpress.com/2018/07/24/evo…
Read 36 tweets

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