Cloud-based EAs: An algorithmic study Juan-J. Merelo,  Maribel García-Arenas, Antonio M. Mora,  Pedro Castillo, Gustavo Ro...
IDEA <ul><li>What do you know about cloud storage services?
Why not to use them for computing?
How can we use all our computers for make a multi-computer? </li><ul><li>Desktop computer
Portable computer
Home computer
Any other ... </li></ul></ul>
How to test the idea <ul><li>Look for some storage FREE services </li><ul><li>What are their _real_ services and what is t...
Synchronization ? </li></ul><li>What about Dropbox?  </li></ul></ul>
Dropbox  TM <ul><li>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 volunteer for computation
It monitor the local filesystem and uploads information asynchronously
We can use it as a local directory </li></ul>
Putting in practice with Evolutionary Computation <ul><li>What we need to make Evolutionary algorithms? </li><ul><li>Excha...
Lets go with the distributed algorithm <ul><li>Dropbox makes the communications
It synchronizes the file-individuals with others computers
Each computer evolves an island
Dropbox folder is a pool of individuals and each computer adds and gets file-individuals from it  </li></ul>
Lets go with the algorithm (II) <ul><li>Each computer connected of synchronized by Dropbox is part of a multi-computer
All the computer evolves a population of individuals and it exchanges with the pool file-individuals </li></ul>
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Dropbox CEC 2011 New Orleans

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How to use Dropbox (TM) for parallel evolutionary computation.

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Dropbox CEC 2011 New Orleans

  1. 1. Cloud-based EAs: An algorithmic study Juan-J. Merelo, Maribel García-Arenas, Antonio M. Mora, Pedro Castillo, Gustavo Romero, JLJ Laredo
  2. 2. IDEA <ul><li>What do you know about cloud storage services?
  3. 3. Why not to use them for computing?
  4. 4. How can we use all our computers for make a multi-computer? </li><ul><li>Desktop computer
  5. 5. Portable computer
  6. 6. Home computer
  7. 7. Any other ... </li></ul></ul>
  8. 8. How to test the idea <ul><li>Look for some storage FREE services </li><ul><li>What are their _real_ services and what is the availability for </li><ul><li>Store and Share information
  9. 9. Synchronization ? </li></ul><li>What about Dropbox? </li></ul></ul>
  10. 10. Dropbox TM <ul><li>It is free up to a certain level of use (measured in traffic and usage)
  11. 11. It is popular, so many people use it, and we may found many volunteer for computation
  12. 12. It monitor the local filesystem and uploads information asynchronously
  13. 13. We can use it as a local directory </li></ul>
  14. 14. Putting in practice with Evolutionary Computation <ul><li>What we need to make Evolutionary algorithms? </li><ul><li>Exchange individuals: Phenotype and Genotype </li></ul><li>We can exchange this information via files. So the name of the file represents the phenotype and genotype and all connected PCs can see it sharing with Dropbox </li></ul>
  15. 15. Lets go with the distributed algorithm <ul><li>Dropbox makes the communications
  16. 16. It synchronizes the file-individuals with others computers
  17. 17. Each computer evolves an island
  18. 18. Dropbox folder is a pool of individuals and each computer adds and gets file-individuals from it </li></ul>
  19. 19. Lets go with the algorithm (II) <ul><li>Each computer connected of synchronized by Dropbox is part of a multi-computer
  20. 20. All the computer evolves a population of individuals and it exchanges with the pool file-individuals </li></ul>
  21. 21. File-individuals <ul><li>How to include phenotype and genotype into a file </li><ul><li>Inside? It is not a good idea
  22. 22. Into the filesystem attributes? Dropbox is working on that and we will testing in the future
  23. 23. Into the namefile? It is our approach </li></ul></ul>
  24. 24. File-individuals (II) <ul><li>The namefile problems </li><ul><li>How many gens can we include into the name?
  25. 25. We have to code the genotype into 32 base
  26. 26. Ex: 00000 -> 0, 00001-> 1, 01010->A ... 111111->V </li></ul><li>The name file includes: Fitness, genotypeBase32codification and the computer which generates the individual </li></ul>
  27. 27. Island Algorithm <ul><li>Creates and evaluates the initial population
  28. 28. Until find the problem solution </li><ul><li>Breed the population
  29. 29. Evaluate
  30. 30. Generational replacement with 1-elitism
  31. 31. If it is time, Gets and file-individual from the pool and incorporates it to the population
  32. 32. It it is time, Adds the best or a random file-individual to the pool </li></ul><li>Adds the best individual to the pool </li></ul>
  33. 33. Goals <ul><li>What want we test? </li><ul><li>It is Dropbox util for distributed evolutonary algorithms based on pool? </li></ul><li>How can we test it? </li><ul><li>Making an distributed evolutionary algorithm based on pool and demostrating that Dropbox synchronized architecture can run it as a multi-computer </li></ul></ul>
  34. 34. Problems: MMDP <ul><li>Multimodal Deceptive Problem
  35. 35. It is composed of k (k=80) subproblems of 6 bits each one called s i for i=0 to 79 .
  36. 36. Depending of the number of ones s i takes the values detailed into the table
  37. 37. The optimum fitness for this problem is 80 </li></ul>ones fitness 0 or 6 1 5 or 1 0 2 or 4 0,360384 3 0,640576
  38. 38. Problems: P-Peaks <ul><li>This problem is created by generating P random N-bit strings where the fitness value of an individual is the number of bits that it has in common with the nearest peak divided by N
  39. 39. For this time, P is 300 and N is 600 and H is the hamming distance between x and the i-peak
  40. 40. The optimum fitness for this problem is 1. </li></ul>
  41. 41. Parameters <ul><li>We use as multi-computer one, two or four heterogeneous computers so we use one, two or four island
  42. 42. Population size: 1000 individuals
  43. 43. Selection: 3-tournament
  44. 44. Crossover: uniform
  45. 45. Mutation: bit-flit
  46. 46. Replacement: Generational with 1-elitism
  47. 47. Stop criteria: maximum number of evaluations or to reach the solution of the problem </li></ul>
  48. 48. Parameters (II) <ul><li>Migration </li><ul><li>MMDP: This problem is solved usually around 3000 generations including only one computer and we will include in and out migration each 100, 200 and 400 generations so we migrate around 30, 15 or only 7 times during the evolution
  49. 49. P-Peaks: This problem is solved usually around 165 generations including only one computer and we will include in and out migration each 20, 40 and 60 generations so we migrate around 8, 4 or 2 times during the evolutionary process </li></ul></ul>
  50. 50. First results: MMDP Computers Gens for Migration success(%) 1 100 83% 2 100 95% 4 100 100% 1 200 70% 2 200 88% 4 200 100% 1 400 80% 2 400 90% 4 400 100%
  51. 51. First results: P-PEAKS Computers Gens for Migration success(%) 1 20 100% 2 20 100% 4 20 100% 1 40 100% 2 40 100% 4 40 100% 1 60 100% 2 60 100% 4 60 100% <ul><li>There is no difference for the success rate </li></ul>
  52. 52. Second results: MMDP
  53. 53. Second results: P-Peaks
  54. 54. Questions

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