1. Using Free Cloud Storage Services For Distributed Evolutionary Algorithms Maribel García-Arenas, Juan-J. Merelo, Antonio M. Mora, Pedro Castillo

3. Dropbox features

4. Putting in practice with Evolutionary Computation

5. File-individuals

6. Island Algorithm

7. Goals

8. Problems

9. Results

11. Why not use them for computing?

13. Portable computer

14. Home computer

15. Any other computers...

17. After that, We have selected Dropbox

19. It is popular , so many people use it, and we may found many volunteers for computation

20. It monitors the local filesystem and uploads information asynchronously

21. It looks like a local directory

24. It synchronizes the file-individuals with other computers

25. Each computer evolves an island

26. Dropbox folder contains a pool of individuals and each computer adds and gets file-individuals from it

28. Each Island-computer evolves a population of individuals and exchanges with the pool file-individuals when the migration process must be done

30. Into the filesystem attributes ? Dropbox is working on that and we will be testing in the future

31. Into the filename ? It is our approach

33. We have to code the genotype into base 32

37. Evaluate

38. Generational replacement with 1-elitism

39. After a fixed number of generations, Immigrate (gets one file-individual from the pool and incorporates it to the population)

42. Each computer looks for this kind of file within the Dropbox folder and adds the total of evaluations.

43. When the sum of this evaluations is greater than the fixed minimum, the evolution of this island ends.

46. It is composed of k (k=80) subproblems of 6 bits each one called s i for i=0 to 79 .

47. Depending of the number of ones s i takes the values detailed into the table ones fitness 0 or 6 1 5 or 1 0 2 or 4 0,360384 3 0,640576

49. For our problem, the trap is defined for l=4, a=3, b=4 and z = 3

50. With 30 traps

51. into the genome {

53. Population size: 1000 individuals

54. Selection: Tournament

55. Crossover: uniform

56. Mutation: bit-flit

57. Replacement: Generational with 1-elitism

58. Stop criteria: minimum number of evaluations for the multi-computer

59. WiFi with WPA/Enterprise encryption.

60. Results for MMDP

61. Results for TRAP

63. With this approach everyone can use a multicomputer running an evolutionary algorithm with a good scaling behavior.

64. Others results for MMDP

65. Questions