Analysing the Performance of Different Population Structures for an Agent-based Evolutionary Algorithm
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  • 1. IntroductionP2P in aNutshellThe EvolvableAgentExperimentalAnalysis Analysing the Performance of DifferentGoalsMethodologyAnalysis of Population Structures for an Agent-basedResultsConclusions Evolutionary AlgorithmFuture Works Juan Luis Jim´nez Laredo et al. e Dpto. Arquitectura y Tecnolog´ de Computadores ıa Universidad de Granada 18-Jan-2011 1 / 17
  • 2. ScopeIntroductionP2P in aNutshellThe EvolvableAgentExperimentalAnalysisGoals • Status: Peer-to-Peer Evolutionary Computation (P2P EC)MethodologyAnalysis ofResults represents a parallel solution for hard problemsConclusions optimizationFuture Works • Modelling: Fine grained parallel EA using a P2P protocol as underlying population structure • Objective: Comparison of different population structures on the EA performance 2 / 17
  • 3. OutlineIntroductionP2P in aNutshellThe EvolvableAgent 1 IntroductionExperimentalAnalysis P2P in a NutshellGoalsMethodology The Evolvable AgentAnalysis ofResultsConclusions 2 Experimental AnalysisFuture Works Goals Methodology Analysis of Results 3 Conclusions 4 Future Works 3 / 17
  • 4. OutlineIntroductionP2P in aNutshellThe EvolvableAgent 1 IntroductionExperimentalAnalysis P2P in a NutshellGoalsMethodology The Evolvable AgentAnalysis ofResultsConclusions 2 Experimental AnalysisFuture Works Goals Methodology Analysis of Results 3 Conclusions 4 Future Works 4 / 17
  • 5. P2P in a NutshellIntroductionP2P in aNutshellThe EvolvableAgentExperimental P2P ECAnalysisGoals • Virtualization:MethodologyAnalysis ofResults Single view atConclusions application levelFuture Works • Decentralization: No central management • Massive Scalability: Up to thousands of computers 5 / 17
  • 6. OutlineIntroductionP2P in aNutshellThe EvolvableAgent 1 IntroductionExperimentalAnalysis P2P in a NutshellGoalsMethodology The Evolvable AgentAnalysis ofResultsConclusions 2 Experimental AnalysisFuture Works Goals Methodology Analysis of Results 3 Conclusions 4 Future Works 6 / 17
  • 7. The Evolvable Agent ModelIntroductionP2P in aNutshell Design principlesThe EvolvableAgent • Agent based approachExperimentalAnalysis • Fine grain parallelizationGoals • Spatially structured EAMethodologyAnalysis ofResults • Local selectionConclusionsFuture Works 7 / 17
  • 8. The Evolvable Agent ModelIntroductionP2P in aNutshell Design principlesThe EvolvableAgent • Agent based approachExperimentalAnalysis • Fine grain parallelizationGoals • Spatially structured EAMethodologyAnalysis ofResults • Local selectionConclusionsFuture Works 7 / 17
  • 9. OutlineIntroductionP2P in aNutshellThe EvolvableAgent 1 IntroductionExperimentalAnalysis P2P in a NutshellGoalsMethodology The Evolvable AgentAnalysis ofResultsConclusions 2 Experimental AnalysisFuture Works Goals Methodology Analysis of Results 3 Conclusions 4 Future Works 8 / 17
  • 10. Goals and Test-CasesIntroductionP2P in aNutshellThe EvolvableAgentExperimental GoalAnalysisGoals • Comparison of performances using different populationMethodologyAnalysis ofResults structuresConclusionsFuture Works Ring Watts-Strogatz Newscast 9 / 17
  • 11. OutlineIntroductionP2P in aNutshellThe EvolvableAgent 1 IntroductionExperimentalAnalysis P2P in a NutshellGoalsMethodology The Evolvable AgentAnalysis ofResultsConclusions 2 Experimental AnalysisFuture Works Goals Methodology Analysis of Results 3 Conclusions 4 Future Works 10 / 17
  • 12. Experimental settingsIntroductionP2P in aNutshellThe EvolvableAgentExperimentalAnalysis • 2-Trap. L=12...60GoalsMethodology • Population sizeAnalysis ofResults • Estimated by bisectionConclusions • SelectorecombinativeFuture Works GA (Mutation less) • Minimum population size able to reach 0.98 of SR • Uniform Crossover • Binary Tournament 11 / 17
  • 13. OutlineIntroductionP2P in aNutshellThe EvolvableAgent 1 IntroductionExperimentalAnalysis P2P in a NutshellGoalsMethodology The Evolvable AgentAnalysis ofResultsConclusions 2 Experimental AnalysisFuture Works Goals Methodology Analysis of Results 3 Conclusions 4 Future Works 12 / 17
  • 14. Population StructureIntroductionP2P in aNutshellThe EvolvableAgentExperimental SettingsAnalysisGoalsMethodology Problem instance: 2-trapAnalysis ofResults Pop. Size: Tuning AlgorithmConclusions No MutationFuture Works 13 / 17
  • 15. Population StructureIntroductionP2P in aNutshell SettingsThe EvolvableAgent Problem instance: L=60 2-trapExperimentalAnalysis Pop. Size: 135GoalsMethodology Max. Eval: 5535Analysis of 1Results Mutation: Bit-flip Pm = LConclusionsFuture Works 14 / 17
  • 16. ConclusionsIntroductionP2P in aNutshellThe EvolvableAgentExperimentalAnalysisGoalsMethodology • Regular lattices require of smaller population sizesAnalysis ofResults ... BUT a bigger number of evaluations to find a solution.Conclusions • Different small-world methods produce an equivalentFuture Works performance ...That’s good! Many P2P protocol are designed to work as small-world networks (i.e. Interoperability/Migration between P2P platforms) 15 / 17
  • 17. Future WorksIntroductionP2P in aNutshellThe EvolvableAgentExperimentalAnalysisGoalsMethodologyAnalysis ofResults • Validation of the model in a real P2P infrastructureConclusions • Exploration of other P2P protocols as populationFuture Works structures • Extension of the P2P concept to other metaheuristics 16 / 17
  • 18. QuestionsIntroductionP2P in aNutshellThe EvolvableAgentExperimentalAnalysisGoalsMethodologyAnalysis ofResultsConclusions Thanks for your attention!Future Works 17 / 17