This document discusses using cloud storage services like Dropbox to enable distributed evolutionary algorithms across multiple computers. It proposes using Dropbox to synchronize file-individuals representing phenotypes and genotypes between computers. Each computer would evolve its own population while exchanging individuals with a shared Dropbox folder. The document tests this approach on two problems using one, two, or four computers with varying migration frequencies. Results show higher success rates with more computers and less frequent migration.

1. Cloud-based EAs: An algorithmic study Juan-J. Merelo, Maribel García-Arenas, Antonio M. Mora, Pedro Castillo, Gustavo Romero, JLJ Laredo

3. Why not to use them for computing?

5. Portable computer

6. Home computer

7. Any other ...

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

12. It monitor the local filesystem and uploads information asynchronously

13. We can use it as a local directory

16. It synchronizes the file-individuals with others computers

17. Each computer evolves an island

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

20. All the computer evolves a population of individuals and it exchanges with the pool file-individuals

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

23. Into the namefile? It is our approach

25. We have to code the genotype into 32 base

29. Evaluate

30. Generational replacement with 1-elitism

31. If it is time, Gets and file-individual from the pool and incorporates it to the population

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

36. Depending of the number of ones s i takes the values detailed into the table

37. The optimum fitness for this problem is 80 ones fitness 0 or 6 1 5 or 1 0 2 or 4 0,360384 3 0,640576

39. For this time, P is 300 and N is 600 and H is the hamming distance between x and the i-peak

40. The optimum fitness for this problem is 1.

42. Population size: 1000 individuals

43. Selection: 3-tournament

44. Crossover: uniform

45. Mutation: bit-flit

46. Replacement: Generational with 1-elitism

47. Stop criteria: maximum number of evaluations or to reach the solution of the problem

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

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%

52. Second results: MMDP

53. Second results: P-Peaks

54. Questions