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

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

2. Outline
1) Idea and how to test it
2) Dropbox features
3) Putting in practice with Evolutionary Computation
4) File-individuals
5) Island Algorithm
6) Goals
7) Problems
8) Results
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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...
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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
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5. TM
Dropbox 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
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6. Putting in practice with
Evolutionary 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
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7. Let's 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
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8. Let's 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
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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
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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
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11. Island Algorithm
1.Creates and evaluates the initial population
2.Until to reach a number of evaluations into the
multi-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
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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.
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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.
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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
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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
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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.
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17. Results for MMDP
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18. Results for TRAP
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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.
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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
60
Percentage
150000
40
100000
20
50000
0 0
100 200 400 1 2 4
Migration frecuency
Islands
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21. Questions
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