Parallel Problem Solving from Nature session-4
•Als ODP, PDF herunterladen•
0 gefällt mir•378 views
Introduction to 14 papers in session 4 of PPSN http://home.agh.edu.pl/~ppsn/
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie Parallel Problem Solving from Nature session-4
Ähnlich wie Parallel Problem Solving from Nature session-4 (20)
Mehr von Juan J. Merelo
Mehr von Juan J. Merelo (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Parallel Problem Solving from Nature session-4
- 1. Session 4
- 3. We are all connected
- 4. Let's solve the Travelling Posterviewer problem
- 5. Careful! Don't touch here!
- 7. A model has to be found to explain that behavior.
- 10. Next: application to Paul
- 14. Akaike information criteria used as fitness
- 17. MOEA using returns + statistical significance of rule.
- 20. Cellular better than not (except for JADE)
- 24. n objectives -> n – 1 constraints + 1 objective
- 25. COP solution not shown in paper
- 28. Selfish gene hypothesis: strategy successful if it has been able to spread over a population.
- 34. Sequential, mirrored versions of ES and CMA-ES faster and more robust
- 36. Crossover with repair mechanisms improves optimization time.
- 37. Feasible parent solution even more: O n 3 log n
- 40. Is source code available? No, it's not! That's not good! Except papers #228 and #146!
- 41. Thank you very much Happy postering!
Hinweis der Redaktion
- Thanks to Tomas Runarsson, who invited me and funded my stay here
- Created using wordle, a tagcloud creator, which, unfortunateluy, is not open source
- Graph elaborated from EasyChair data using UciNet and NetDraw. Funny thing everything is connected. Normally sessions are chosen for topical closeness, but it needn't really happen. Not that many keywords, either, out of 80 possible ones.
- http://www.flickr.com/photos/hikingartist/3009540273/ This class of balance: keeping two guys balanced on a pole while using a ballet skirt, could be discovered by this algorithm. Eventually. It's just as well this paper is the first, since it's one of the most difficult to understand.
- Obtained from http://www.flickr.com/photos/jauladeardilla/3951932940/sizes/l/in/photostream/ As you see, this octopus arm has an infinite number of segments, which will be kind of difficult to evolve I could have used Paul the Octopus, but that would have been too easy, don't cha think? Paul's tentacles will be optimized so that it can choose England over, well, over somebody else.
- http://www.flickr.com/photos/moff/4092989093/ He's the travelling sweatman, and he's spreading pheromones all over the place. I can smell it from here.
- http://www.flickr.com/photos/clickflashphotos/4897003658/ These model trees have been walking the catwalk, or the treewalk, with some difficulty, I tell you, to show all of you, as a premiere, the latest fashion in tree attire
- Talking about soccer, there's nothing more optimized that the Spanish team. No way English soccer, even if it's perfectly scheduled, can beat that.
- http://www.flickr.com/photos/damejiar/2677123803/ She's showing her how to trade. “You see? Here's the avocado. Now you say 'How much is that beautiful avocado'”?
- http://www.flickr.com/photos/euthman/426956752/ These cells are evolving differently. One of them is bigger, and the other smaller. Though both are purple. Color change will have to wait for the next generation.
- http://www.flickr.com/photos/erwyn/4202089930/ This Cupid is obviously directionally-biased : it's pointing at you
- This boat's constraint is optimized... and blue http://www.flickr.com/photos/melodysk/1149713138/ No statistical significance is shown, either...
- http://www.flickr.com/photos/kenyee/239813655/ These agents seem quite autonomous, although they seem to have some trouble with, well, zombies.
- http://www.flickr.com/photos/tirralirra/2062189596/ This is rather geometric cross-stitch, but it also deserves optimization, right?
- http://www.flickr.com/photos/gi/435888435/ She's got a sequence of mirrors, and she's got a strategy to try them. Mirrored sampling adds/subtracts a random vector to the parent, instead of only adding it. Sequential selection selects one as soon as it's found to be better, does not wait for the best. Saves evaluations.
- http://www.flickr.com/photos/hazeljoy/3798639516/ Here's an instance of the all-pairs problem. You have all those pairs, and a single pair of feet. Which one should you try?
- http://www.flickr.com/photos/ferrous/363972144/ You probably don't need so many clocks to analyze time, and in this paper they are going to show you how.