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Presentación del artículo titulado "Using Free Cloud Storage Services For Distributed
Evolutionary Algorithms" publicado en GECCO 2011

Published in: Technology
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  1. 1. Using Free Cloud Storage Services For Distributed Evolutionary AlgorithmsMaribel García-Arenas,Juan-J. Merelo,Antonio M. Mora,Pedro Castillo
  2. 2. Outline1) Idea and how to test it2) Dropbox features3) Putting in practice with Evolutionary Computation4) File-individuals5) Island Algorithm6) Goals7) Problems8) Results 2
  3. 3. IDEA• What do you know about cloud storage services?• Why not use them for computing?• How can we use all our computers to make a multicomputer? – Desktop computer – Portable computer – Home computer – Any other computers... 3
  4. 4. How to test the idea• Look for some free storage services and test them: What are their features and what is the availability for storing, sharing and synchronizing information• After that, We have selected Dropbox 4
  5. 5. TMDropbox features• It is free up to a certain level of use (measured in traffic and usage)• It is popular, so many people use it, and we may found many volunteers for computation• It monitors the local filesystem and uploads information asynchronously• It looks like a local directory 5
  6. 6. Putting in practice withEvolutionary Computation • What do we need to build Evolutionary Distributed Algorithms? – Exchange individuals among populations: Phenotype and Genotype • We can exchange this information using files. So the name of the file represents the phenotype and genotype and all connected PCs share it with Dropbox 6
  7. 7. Lets go• File distribution via Dropbox• It synchronizes the file-individuals with other computers• Each computer evolves an island• Dropbox folder contains a pool of individuals and each computer adds and gets file-individuals from it 7
  8. 8. Lets go (II)• Each computer connected or synchronized by Dropbox is part of a multi-computer• Each Island-computer evolves a population of individuals and exchanges with the pool file-individuals when the migration process must be done 8
  9. 9. File-individuals• How to include phenotype and genotype into a file – As the contents of the file? It is not a good idea because we have to open and close files and Dropbox has to synchonize them. – Into the filesystem attributes? Dropbox is working on that and we will be testing in the future – Into the filename? It is our approach 9
  10. 10. File-individuals (II)• The filename problem – How many gens can we include into the name? – We have to code the genotype into base 32 – Ex: 00000 → 0, 00001-> 1, 01010->A ... 111111->V• The filename includes: Fitness, genotypeBase32codification and the id of the computer which generates the individual 10
  11. 11. Island Algorithm1.Creates and evaluates the initial population2.Until to reach a number of evaluations into themulti-computer • Breed the population • Evaluate • Generational replacement with 1-elitism • After a fixed number of generations, Immigrate (gets one file-individual from the pool and incorporates it to the population) • After a fixed number of generations, Migrate (adds the best or a random file-individual to the pool)3.Adds the best individual to the pool 11
  12. 12. Control of the number of evaluations• Each computer creates a file whose name is the number of evaluations performed and its identification (random initial seed)• Each computer looks for this kind of file within the Dropbox folder and adds the total of evaluations.• When the sum of this evaluations is greater than the fixed minimum, the evolution of this island ends. 12
  13. 13. Goals• What do we want to test? – We want test if we save time when use the multi-computer for computing a fixed number of evaluations.• How can we test it? – Making a distributed evolutionary algorithm based on pool and testing that the time for reaching the fixed evaluations decreases when you add new nodes to our multi- computer linked by Dropbox. 13
  14. 14. Problems: MMDP• Multimodal Deceptive Problem• It is composed of k (k=80) subproblems of 6 bits each one called si for i=0 to 79.• Depending of the number of ones si takes the values ones fitness 0 or 6 1 detailed into the table 5 or 1 0 k 2 or 4 0,360384 Fitnessindividual =∑i=1 fitness s  i 3 0,640576 14
  15. 15. Problems: TRAP• It is defined for the unitation function (number of ones in a binary string) using the following function. { if u     z a  z−u  , x x trap u  = x b z l − z u − z  , x otherwise• For our problem, the trap is defined for l=4, a=3, b=4 and z = 3• With 30 traps into the genome 15
  16. 16. Parameters• We use as multi-computer one, two or four heterogeneous computers so we use one, two, three or four island• Population size: 1000 individuals• Selection: Tournament• Crossover: uniform• Mutation: bit-flit• Replacement: Generational with 1-elitism• Stop criteria: minimum number of evaluations for the multi-computer• WiFi with WPA/Enterprise encryption. 16
  17. 17. Results for MMDP 17
  18. 18. Results for TRAP 18
  19. 19. Conclusions• The Dropbox File-storage and sharing system, can be used as a migration device for distributed evolutionary computation experiments without needing to acquire or set up complicated cloud or grid infrastructure.• With this approach everyone can use a multicomputer running an evolutionary algorithm with a good scaling behavior. 19
  20. 20. Others results for MMDP Time to find the solution Success Rate MMDP Problem 120 300000 100 250000 80 200000 100 1 200 2 400 Time(miliseconds) 4 60Percentage 150000 40 100000 20 50000 0 0 100 200 400 1 2 4 Migration frecuency Islands 20
  21. 21. Questions 21