Service Oriented Architecture for Adaptive Evolutionary Algorithms: Implementation and Applications PhD Dissertation by Pablo García Sánchez University of Granada Advisors Jesús González Peñalver Juan Julián Merelo Guervós Alberto Prieto Espinosa 16/06/2014 domingo 15 de junio de 2014
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Service Oriented Architecture for Adaptive Evolutionary Algorithms
PhD dissertation for the thesis of Pablo García Sánchez "Service Oriented Architecture for Adaptive Evolutionary Algorithms".
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Service Oriented Architecture for Adaptive Evolutionary Algorithms: Implementation and Applications
PhD Dissertation by Pablo García SánchezUniversity of Granada
• 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.
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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.