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How to use Dropbox (TM) for parallel evolutionary computation.
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2. IDEA
3. Why not to use them for computing? 4. How can we use all our computers for make a multi-computer?
5. Portable computer 6. Home computer 7. Any other ... 8. How to test the idea
9. Synchronization ? What about Dropbox? 10. DropboxTM
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 14. Putting in practice with Evolutionary Computation
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 15. Lets go with the distributed algorithm
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 19. Lets go with the algorithm (II)
20. All the computer evolves a population of individuals and it exchanges with the pool file-individuals 21. 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 24. File-individuals (II)
25. We have to code the genotype into 32 base 26. Ex: 00000 -> 0, 00001-> 1, 01010->A ... 111111->V The name file includes: Fitness,genotypeBase32codification and the computer which generates the individual 27. Island Algorithm
28. Until find the problem solution
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 32. It it is time, Adds the best or a random file-individual to the pool Adds the best individual to the pool 33. Goals
How can we test it?
34. Problems: MMDP
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 itakes 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 38. Problems: P-Peaks
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. 41. Parameters
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 48. Parameters (II)
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% 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%
52. Second results: MMDP 53. Second results: P-Peaks 54. Questions