Laredo, Merelo, Fernandes, Mora, Arenas, Castillo y García Sánchez

1. Introduction
Model Design
The Evolvable
Agent
Experimental
Analysis
Influence of the Population Structure on the
Goals
Methodology
Analysis of
Performance of an Agent-based Evolutionary
Results
Conclusions
Algorithm
Conclusions
Future Works
J.L.J. Laredo et al.
Dpto. Arquitectura y Tecnolog´ de Computadores
ıa
Universidad de Granada
11-Sept-2010
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2. Scope
Introduction
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals • Status: Peer-to-Peer Evolutionary Computation (P2P EC)
Methodology
Analysis of
Results
represents a parallel solution for hard problems
Conclusions optimization
Conclusions
Future Works
• Modelling: Fine grained parallel EA using a P2P protocol
as underlying population structure
• Objective: Comparison of different population structures
on the EA performance
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3. Outline
Introduction
Model Design
The Evolvable
Agent
1 Introduction
Experimental
Analysis 2 Model Design
Goals
Methodology The Evolvable Agent
Analysis of
Results
Conclusions 3 Experimental Analysis
Conclusions
Goals
Future Works
Methodology
Analysis of Results
4 Conclusions
Conclusions
5 Future Works
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4. Introduction
Introduction
Model Design
The Evolvable
Agent
Experimental P2P EC
Analysis
Goals • Virtualization:
Methodology
Analysis of Single view at
Results
Conclusions
application level
Conclusions
• Decentralization:
Future Works
No central
management
• Massive Scalability:
Up to thousands of
computers
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5. Population Structure as a complex network
Introduction
Panmictic Small-world Regular lattice
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results
Conclusions
Conclusions
Future Works
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6. Population Structure as a complex network
Introduction
Panmictic Small-world Regular lattice
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results
Conclusions
Conclusions
Future Works
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7. Population Structure as a complex network
Introduction
Panmictic Small-world Regular lattice
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results
Conclusions
Conclusions
Future Works
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8. Population Structure as a complex network
Introduction
Panmictic Small-world Regular lattice
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results n(n−1)
2
log(n) n
Conclusions
Conclusions
Future Works
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9. Outline
Introduction
Model Design
The Evolvable
Agent
1 Introduction
Experimental
Analysis 2 Model Design
Goals
Methodology The Evolvable Agent
Analysis of
Results
Conclusions 3 Experimental Analysis
Conclusions
Goals
Future Works
Methodology
Analysis of Results
4 Conclusions
Conclusions
5 Future Works
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10. The Evolvable Agent Model
Introduction
Model Design Design principles
The Evolvable
Agent • Agent based approach
Experimental
Analysis
• Fine grain parallelization
Goals • Spatially structured EA
Methodology
Analysis of • Local selection
Results
Conclusions
Conclusions
Future Works
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11. The Evolvable Agent Model
Introduction
Model Design Design principles
The Evolvable
Agent • Agent based approach
Experimental
Analysis
• Fine grain parallelization
Goals • Spatially structured EA
Methodology
Analysis of • Local selection
Results
Conclusions
Conclusions
Future Works
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12. Outline
Introduction
Model Design
The Evolvable
Agent
1 Introduction
Experimental
Analysis 2 Model Design
Goals
Methodology The Evolvable Agent
Analysis of
Results
Conclusions 3 Experimental Analysis
Conclusions
Goals
Future Works
Methodology
Analysis of Results
4 Conclusions
Conclusions
5 Future Works
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13. Goals and Test-Cases
Introduction
Model Design
The Evolvable
Agent
Experimental Goal
Analysis
Goals • Comparison of performances using different population
Methodology
Analysis of
Results
structures
Conclusions
Conclusions
Ring Watts-Strogatz Newscast
Future Works
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14. Outline
Introduction
Model Design
The Evolvable
Agent
1 Introduction
Experimental
Analysis 2 Model Design
Goals
Methodology The Evolvable Agent
Analysis of
Results
Conclusions 3 Experimental Analysis
Conclusions
Goals
Future Works
Methodology
Analysis of Results
4 Conclusions
Conclusions
5 Future Works
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15. Experimental settings
Introduction
Model Design
The Evolvable
Agent
Experimental
Analysis • 2-Trap. L=12...60
Goals
Methodology • Population size
Analysis of
Results • Estimated by bisection
Conclusions • Selectorecombinative
Conclusions
Future Works
GA (Mutation less)
• Minimum population
size able to reach 0.98
of SR
• Uniform Crossover
• Binary Tournament
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16. Outline
Introduction
Model Design
The Evolvable
Agent
1 Introduction
Experimental
Analysis 2 Model Design
Goals
Methodology The Evolvable Agent
Analysis of
Results
Conclusions 3 Experimental Analysis
Conclusions
Goals
Future Works
Methodology
Analysis of Results
4 Conclusions
Conclusions
5 Future Works
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17. Population Structure
Introduction
Model Design
The Evolvable
Agent
Experimental Settings
Analysis
Goals Problem instance: 2-trap
Methodology
Analysis of
Results
Pop. Size: Tuning Algorithm
Conclusions No Mutation
Conclusions
Future Works
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18. Population Structure
Introduction
Model Design Settings
The Evolvable
Agent
Problem instance: L=60 2-trap
Experimental
Analysis Pop. Size: 135
Goals
Methodology
Max. Eval: 5535
1
Analysis of
Results Mutation: Bit-flip Pm = L
Conclusions
Conclusions
Future Works
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19. Conclusions
Introduction
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals • Regular lattices require of smaller population sizes
Methodology
Analysis of
Results
... BUT a bigger number of evaluations to find a solution.
Conclusions • Different small-world methods produce an equivalent
Conclusions
Future Works
performance
...That’s good! Many P2P protocol are designed to work
as small-world networks
(i.e. Interoperability/Migration between P2P platforms)
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20. Future Works
Introduction
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results • Validation of the model in a real P2P infrastructure
Conclusions
Conclusions
• Exploration of other P2P protocols as population
Future Works structures
• Extension of the P2P concept to other metaheuristics
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21. Questions
Introduction
Model Design
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results
Conclusions
Conclusions
Thanks for your attention!
Future Works
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