parallel problem solving from nature session-4
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Session 4
Thanks to Tomas Runarsson, who invited me and funded my stay here
View from above
Created using wordle, a tagcloud creator, which, unfortunateluy, is not open source
We are all connected
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.
Let's solve the Travelling Posterviewer problem
Careful! Don't touch here!
Paper #190: Evolutionary Detection of New Classes of
Equilibria.
Application in Behavioral Games
D. Dumitrescu et al.
People play games in a weird way.
A model has to be found to explain that behavior.
Multiobjective algorithm is used to evolve strategies that approach that behaviorWhich MOEA?
For those into games
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.
Paper #68: Evolving a Single Scalable Controller for an
Octopus
Arm with a Variable Number of Segments
Woolley & Stanley.
Size is not so important In terms of #dimensions of state/action space
A indirectly encoded neurocontroller (HyperNEAT) for an octopus arm is evolvedFitness: ability in grasping a target
A scalable neurocontroller architecture is found.
Next: application to Paul
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 evolveI 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.
Paper #104: Evolving Strategies for Updating Pheromone
Trails: a Case Study with the TSP
Tavares & Pereira
Let GP devise your ACO pheromone updating strategy for you.
Weird and interesting update strategies ariseThey generalize better than other update strategies
Terminal set is key (but not too open to experiments)
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.
Paper #126: Globally Induced Model Trees:
An Evolutionary Approach
Czajkowski & Krtowski
Regression trees useful for extracting patterns in databasesWhat are regression trees?
Usual: top-down approach. Bad. Here: global approach based on EAHeuristic mutation/crossover operators used
Akaike information criteria used as fitness
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
Paper #211: Scheduling English Football Fixtures over the
Holiday Period Using Hyper-heuristics
Gibbs et al.
Problem: producing a fixture schedule for English footballLarge problem space!
Solucion: selection hyperheuristicsApplying heuristics over single candidate solution
Reinforcement learning rocks!
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.
Paper #162: Evolutionary Learning of Technical Trading Rules
without Data-mining Bias.
Agapitos et al.
Get rich quick!Predict performance of trading rules from past behavior.Rule performance decays over time
Grammar-based GP used for rule induction.
MOEA using returns + statistical significance of rule.
Encouraring results!
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'?
Paper #208: Differential Evolution Algorithms
with Cellular Populations
Dorronsoro & Bouvry
DE based on updating real-number vectors via combinations of othersRarely used in a distributed environment.
Several DE algorithms tested on a cellular framework.
Cellular better than not (except for JADE)
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.
Paper #70: Hybrid Directional-biased Evolutionary Algorithm for
Multi-objective Optimization
Shimada et al.
Nobody searches the edges of the Pareto front!Pareto would be upset with this
hIDEA adds weights to objectives to allow this.Based on IBEA
(Inverse) General Distance used to evaluate solutions
hIDEA searches wider and closer to Pareto frontPareto would be happy!
http://www.flickr.com/photos/erwyn/4202089930/This Cupid is obviously directionally-biased : it's pointing at you
Paper #80: Solving Multiobjective Optimization Problem by
Constraint Optimization
Jiang & Zhang & Ren
Multiobjective turned into constraint optimization problems Multiobjective EA based on constraint optimization: MEACO
n objectives n 1 constraints + 1 objective
COP solution not shown in paper
2 objectives only?
Spacing/convergence metrics measured.MEACO is the best almost always
This boat's constraint is optimized... and blue http://www.flickr.com/photos/melodysk/1149713138/
No statistical significance is shown, either...
Paper #228: Environment-driven Embodied Evolution
in a Population of Autonomous Agents
Bredeche & Montanier
Implicit fitness
Selfish gene hypothesis: strategy successful if it has been able to spread over a population.
Autonomous agents broadcast their genome while alive, load variation of one of those received.Implicit selection
Autonomous agents improve!
http://www.flickr.com/photos/kenyee/239813655/ These agents seem quite autonomous, although they seem to have some trouble with, well, zombies.
Increasing exploration
Paper #131: One-Point Geometric Crossover
Alberto Moraglio
Crossover can be interpreted geometricallyAnd generalized to any data structure where d is defined.
Crossover results are actually placed over a geodesic between parents.
Crossover operators are proposed for diferent data structuresAnd proofs made in flyspeck font
http://www.flickr.com/photos/tirralirra/2062189596/This is rather geometric cross-stitch, but it also deserves optimization, right?
Paper #146: Mirrored Sampling and Sequential Selection
for Evolution Strategies
Brockhoff & al.
Did you know that single-parent non-elitist evolution strategy (ES) can be locally faster than the (1+1)-ES?But only with mirrored sampling and sequential selection!
Convergence speed theoretically computed.
Sequential, mirrored versions of ES and CMA-ES faster and more robust
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.
Paper #120: More Effective Crossover Operators for the
All-Pairs Shortest Path Problem
Doerr & al.
APSP consists in computing all shortests paths betwen pair of nodes in a weighted graph.
Crossover with repair mechanisms improves optimization time.
Feasible parent solution even more: O n3 log n
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?
Paper #23: General Scheme for Analyzing Running Times
of Parallel Evolutionary Algorithms
Lssig & Sudholt
Theoretical analysis of speed-up of parallel evolutionary algorithms on several functions and over different topologies.
Method based on fitness-level methods.Finding lower-bound for probability of leaving one level
Differences abound.
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.
Is source code available?
No, it's not!That's not good!
Except papers #228 and #146!
Thank you very much
Happy postering!
PPSN 2010Krakw, Monday, September 13th, 2010
JJ MereloDepto. Arquitectura y Tecnologa de los ComputadoresU. Granadajmerelo@geneura.ugr.es
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