service oriented architecture for adaptive evolutionary algorithms
DESCRIPTION
PhD dissertation for the thesis of Pablo García Sánchez "Service Oriented Architecture for Adaptive Evolutionary Algorithms".TRANSCRIPT
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Service Oriented Architecture for Adaptive Evolutionary Algorithms: Implementation and Applications
PhD Dissertation by Pablo García SánchezUniversity of Granada
AdvisorsJesús González PeñalverJuan Julián Merelo GuervósAlberto Prieto Espinosa
16/06/2014
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Heterogeneous and dynamic environments
SOA
SOA-EA
OSGiLiath
Objective 4
Objective 2
Objective 1
Objective 3
Realproblem
Real infrastucture
Heterogeneous and dynamic environments
SOA
SOA-EA
OSGiLiath
Real infrastructure
Objective 4
Objective 2
Objective 1
Objective 3
Realproblem
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Classic distribution approaches in EAs
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New trends
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CC Photo by @draxus
Pool
ClientClient
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Examples of adaptation in algorithms
• Adaptive Memetic Algorithms:
• Hyper-heuristics.
• Multimemes.
• Co-Evolving MAs.
• Adaptation to hardware.
5
GA SA
SA
LS
TS
Individual...
?
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Challenges in EA software (Parejo et al. 2012)
• Standardization.
• Interoperability.
• Dynamic and reflexive capabilities.
• High cost on switching from a framework to other.
• Open Science.
6
Algorithm::Evolutionary
MALLBA jMetal
ECJ
DR-E-AMParadiseEOHeuristicLab
METCOjCLEC
gridUFO
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Heterogeneous and dynamic environments
SOA
SOA-EA
OSGiLiath
Real infrastucture
Objective 4
Objective 2
Objective 1
Objective 3
Realproblem
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Service Oriented Architecture
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Service characteristics
• Differences with Object-Oriented paradigm.
• Discoverable and dynamically bound.
• Self-contained.
• Modular.
• Interoperable (different programming languages).
• Loosely coupled.
• Location transparent.
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Technologies and methodologies for SOA
10
BCM
Specific Ontology Navigation
Content Rendering
Transaction Handling
Business Processes
Collaboration Agreements, MOA
Codelist subsetting Services; Transaction Processing
Communities of Interests- CoI
44
55
66
778899
10 10
11 11
!Copyright (C) OASIS Open 2003-2006. All Rights Reserved.
UMM SOMA
Ali Arsanjani https://www.ibm.com/developerworks/library/ws-soa-design1/
REST Web Services
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SOA as a solution
• Development
• Integration
• Standardization
• Dynamism
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SOA (abstract) GRID (infrastructure)
Web Services
Frameworks for EAs
Evolutionary Robotics
Cloud Computing
Globus
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Heterogeneous and dynamic environments
SOA
SOA-EA
OSGiLiath
Objective 4
Objective 2
Objective 1
Objective 3
Realproblem
Real infrastucture
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SOEAs
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BitFlip
Mutation
Binary
Initialization
Fitness Distributor
Evaluation
TPX
Recombination
Roulette
Selection
UPXN Worst Rep.
Replacement
MMDP calculator
Evaluation
MMDP calculator
Evaluation
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Design issues for SOEAs
• Genericity in EA design (Gagné & Parizeau):
• Representation.
• Fitness.
• Operations.
• Evolutionary model.
• Parameter management.
• Configurable output.
• Restrictions in SOA design (Valipour):
• Dynamic binding.
• Self-contained.
• Location transparency.
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SOA-EA
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Step 1: Identification
• Problems to solve.
• Elements needed.
• Re-utilization.
• Operators.
• Extension points.
• Parameterization.
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Step 2: Specification
• Inputs/Outputs.
• Operations of each services.
• Individual representation.
• Services usage.
• Order of execution.
• Multiple instances.
• Adaptation to machines.
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Specification: Operators
• Flexibility: Recombination can receive more than two individuals.
• Receive interfaces (generic representation).
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RecombinationList of individuals
List of individuals
Crossover
mother, father son1, son2
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Specification: Population, Parameters and Fitness
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Population
- add Individuals - get N Best Individual- get N Worst Individuals- get Size
...
Parameters
- get Parameter- update Parameter- get Parameter Labels
...
Fitness Calculator
List of individualsList of fitnesses
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Specification of the flow
• Reduce the impact of future changes.
• Example of flow: Evolutionary Algorithm implementation (generic evolutionary model).
• Automatic adaptation and binding of the elements.
• Services for hardware adaptation.
20
BitFlip
Mutation
Binary
Initialization
TPX
Recombination
Roulette
Selection
N Worst Rep.
Replacement
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Steps 3 and 4: Implementation and deployment
• Local or remote services.
• Interfaces publication.
• Public or private services.
• Dynamism control.
• Overload of messages.
• Technology selection.
• Security, persistence, benchmarking and monitoring.
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Heterogeneous and dynamic environments
SOA
SOA-EA
OSGiLiath
Objective 4
Objective 2
Objective 1
Objective 3
Realproblem
Real infrastucture
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• Framework to develop SOEAs.
• Includes well-defined interfaces and implementations.
• Paradigm independent.
• Multiple technologies for distribution and discovery.
• Plug-in based.
• Automatic binding.
• Component-oriented.
OSGiLiath
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Example: Basic GA
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Example: NSGA-2
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Example: Master-slave model
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Example: Island model
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Island BIsland A
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Example: Adaptation of operators
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Implementation: Why OSGi?
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OSGi Web Services
Primary environment Local* Internet
Language Java* Any
Interfaces Java Interface* WSDL
Communication protocol Variable SOAP
Dynamism White-Board Explicit
Client creation Previously known Automatic
Extras Plug-in based WS-Extensions
* (by default)
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SOA validation experiments
• Experiment 1: OSGi comparison.
• Experiment 2: Adaptive service binding.
• Experiment 3: Distribution and language independence.
• Experiment 4: Development time between frameworks for EAs.
• Algorithm: canonical GA with same parameters.
• Problems: MMDP and OneMax.
• 30 times for configuration.
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OSGi does not add overload
• Experiment 1: Performance using OSGi.
• Comparing the same EA.
• Solving OneMax problem with the same parameters.
• Stop criterion: fixed number of generations.
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Average solution Average time (s)
OSGiLiath 612.26 ± 6.05 0.19 ± 0.02
OSGiLiath (without OSGi)
613.36 ± 4.50 0.19 ± 0.02
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Service adaptation improves the results
• Experiment 2: Adaptive service binding.
• Solve the MMDP problem.
• Static version vs. adaptive version.
• Stop criterion: optimum found.
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Evolutionary Algorithm
Algorithm
List Population
Population
Selector Gatherer
Parent Selector
Asynchronous Enabler Impl
Asynchronous Enabler
N Tournament
Parent Selector
Roulette
Parent Selector
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Service adaptation improves the results (II)
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Non!adaptive Adaptive0e
+0
01
e+
07
2e
+0
73
e+
07
4e
+0
7
Configuration
Eva
lua
tion
s
Number of evaluations to solve MMDP problem
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OSGiLiath is independent of the programming language
• Experiment 3: Integration with other systems.• Language independence: PHP and Java.• Comparing different communication mechanisms: SOAP
and OSGi ECF generic.• Different number of individuals: 250, 500, 1000, 2000.
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OSGi ECF does not add overhead communication
35
500 1000 1500 2000
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Number of individuals
Seconds
SOAP
OSGi
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SOA saves development time
• Experiment 4: Comparison with other frameworks.
• The same algorithm and parameter configuration in different frameworks (solving OneMax).
36
Name Avg. Solution Avg. Time (s) Lines of Code
OSGiLiath 612.26 ± 6.05 0.19 ± 0.02 10
OSGiLiath (without OSGi)
613.36 ± 4.50 0.19 ± 0.02 103
MALLBA 578.76 ± 7.48 0.16 ± 0.01 2073
ECJ 602.76 ± 6.08 1.40 ± 0.03 5
Algorithm::Evo-lutionary
617.60 ± 12.92 7.78 ± 0.29 41
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Heterogeneous and dynamic environments
SOA
SOA-EA
OSGiLiath
Objective 4
Objective 2
Objective 1
Objective 3
Realproblem
Real infrastucture
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Study on parameter adaptation
• There is not a central control node.
• The number of nodes participating is not limited.
• All nodes automatically bind the available distribution services.
• The nodes must stop when the optimum is found.
• Services must be executed in heterogeneous machines with different operating systems and architectures.
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Automatic binding of nodes
39
Replacer
Basic Replacer
Migrator
Migrator Ring Buffer
Remote nodesLocal Node
Migrator
Migrator
...
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Configurations
• Homogeneous Hardware (HoHa).
• Heterogeneous Hardware (HeHa).
• Homogeneous Size (HoSi): 256 individuals.
• Heterogeneous Size (HeSi): proportional to generations.
• Adaptive Size (AdSi):
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Experimental setting
• Problems to solve: MMDP and OneMax.
• Stop criterion: optimum found.
• Topology: ring.
• Benchmark: number of generations.
• Algorithm: canonical steady-state genetic algorithm.
• The same parameter configuration in all nodes.
• 40 times per configuration.
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Automatic adaptation improves results (MMDP)
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HoHa (Homogeneous Hardware)
HeHa (Heterogeneous Hardware)
HoSi HeSi AdSi
5e+
02
2e+
03
1e+
04
5e+
04
2e+
05
Configuration
Tim
e (
Hete
rogeneous
Hard
ware
)
HoSi HeSi
5e
+02
2e+
03
1e+
04
5e+
04
Configuration
Tim
e (
Hom
ogen
eous
Hard
ware
)
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Average population sizes in AdSi solving MMDP
43
HeN1 HeN2 HeN3 HeN4
0200
400
600
800
1000
Node
Popula
tion S
ize
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Automatic adaptation improves results (OneMax)
44
HoHa (Homogeneous Hardware)
HeHa (Heterogeneous Hardware)
HoSi HeSi
80000
10000
0120000
140000
Configuration
Tim
e (
Hom
ogen
eous
Hard
ware
)
HoSi HeSi AdSi
100000
120000
140000
180000
Configuration
Tim
e (
Hete
rogeneous
Hard
wa
re)
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Average population sizes in AdSi solving OneMax
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HeN1 HeN2 HeN3 HeN4
02
00
40
06
00
80
01
00
0
Node
Po
pu
latio
n S
ize
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Adapting parameters affect all services (MMDP)
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Adapting parameters affect all services (OneMax)
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Adaption improves the results in time
• Adapting online or offline the sub-population size to the computational power of each node yields significantly better results in time.
• The same heterogeneous parameter setting could not improve the results in homogeneous environments.
• The generations in each node is a possible benchmark for parameter setting.
• Changing a parameter can affect all services of the SOEA.
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Heterogeneous and dynamic environments
SOA
SOA-EA
OSGiLiath
Objective 4
Objective 2
Objective 1
Objective 3
Realproblem
Real infrastucture
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Motivation
• RTS games (Planet Wars).
• Genetic Programming.
• Existing competitive (agents): GeneBot (G) and ExpGenebot (E).
• Different node depth: 3,7, Unlimited.
• Validation of the bots in other maps.
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Planet Wars
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Agent generation
• For each planet execute tree.• Based in decisions and actions.• Dynamic fitness: 5 combats vs
Genebot and ExpGenebot.• Crossover/mutation of
branches/tags and rates.
52
actualMyShipsRatio>0.711
attackWeakestNeutralPlanet(0.3)
attackNearestEnemyPlanet(0.2)
attackEnemyBase(0.91)
myShipsLandedFlyingRatio>0.21
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Obtained bots are competitive
• Each generated bot with different maximum depth (3, 7 and U) is tested vs. Genebot (G) and Exp-Genebot (E) in 100 maps.
53Percentage of victories Turns to be defeated
3G 7G UG 3E 7E UE
100
200
300
400
500
Configuration
Turn
s to
be
defe
ate
d
3G 7G UG 3E 7E UE
10
20
30
40
50
60
70
Configuration
Perc
enta
ge o
f vi
ctori
es
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A SOEA obtains competitive bots for RTS games
• A SOEA is used to generate agents for playing Planet Wars RTS game without using human knowledge, using Genetic Programming.
• EA and SOA requirements have been taken into account.
• Obtained bots outperform the one generated by human experts and optimized by a GA.
• Differences in maximum depth.
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Conclusions and outlook
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Conclusions
• EAs can be successfully migrated to SOA and take advantage in dynamic and heterogeneous scenarios.
• The used SOA technology has a huge impact in several issues.
• SOA not force to use distribution services.
• SOA paradigm can be applied successfully to EAs to facilitate the integration, distribution, dynamism and development in some scenarios.
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Contributions (I)
• The Service Oriented Architecture paradigm has been proposed to create distributed, heterogeneous, dynamic and standards-based environments for Evolutionary Algorithms, as it provides mechanisms for interoperability, integration and dynamic control.
• The requirements to develop EAs in the SOA paradigm have been identified.
• These requirements have been taken into account to propose SOA-EA, a methodology that is able to successfully adapt evolutionary algorithms to distributed, heterogeneous, dynamic, standards-based environments.
• Several steps to design all the elements in an EA have been proposed inside this methodology.
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Contributions (and II)
• The methodology has been validated using a specific SOA technology: OSGi.
• A SOA-based implementation (OSGiLiath) of distributed, dynamic, standards-based evolutionary algorithms has been able to solve efficiently different problems.
• As an application of this methodology, two different parameter adaptation schemes of island-based EAs to heterogeneous hardware have been proposed, and an algorithm to obtain competent bots for RTS games has been obtained.
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Future work
• New research line in automatic adaptation of parameters and operators in dynamic and heterogeneous environments under the SOA paradigm.
• More mechanisms to enable/disable services.
• Different benchmarking services.
• Comparison of communication mechanisms.
• Automatic service composition using other technologies.
• Extend the concept of SOEA to other fields.
• New modules and services to address new problems will be added to OSGiLiath.
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Publications related with this thesis
60
Total First author
Journals 5 2
LNCS 5 5
Other peer-reviewed conferences
4 2
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Open Science
• OSGiLiath: https://github.com/fergunet/osgiliath
• Thesis development: https://github.com/fergunet/tesis
• Web page: http://www.osgiliath.org
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Thank you very much!
Questions?
domingo 15 de junio de 2014