1. Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental
Analysis Analysing the Performance of Different
Goals
Methodology
Analysis of
Population Structures for an Agent-based
Results
Conclusions
Evolutionary Algorithm
Future Works
Juan Luis Jim´nez Laredo et al.
e
Dpto. Arquitectura y Tecnolog´ de Computadores
ıa
Universidad de Granada
18-Jan-2011
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2. Scope
Introduction
P2P in a
Nutshell
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
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
P2P in a
Nutshell
The Evolvable
Agent 1 Introduction
Experimental
Analysis P2P in a Nutshell
Goals
Methodology
The Evolvable Agent
Analysis of
Results
Conclusions 2 Experimental Analysis
Future Works Goals
Methodology
Analysis of Results
3 Conclusions
4 Future Works
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4. Outline
Introduction
P2P in a
Nutshell
The Evolvable
Agent 1 Introduction
Experimental
Analysis P2P in a Nutshell
Goals
Methodology
The Evolvable Agent
Analysis of
Results
Conclusions 2 Experimental Analysis
Future Works Goals
Methodology
Analysis of Results
3 Conclusions
4 Future Works
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5. P2P in a Nutshell
Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental P2P EC
Analysis
Goals • Virtualization:
Methodology
Analysis of
Results
Single view at
Conclusions application level
Future Works • Decentralization:
No central
management
• Massive Scalability:
Up to thousands of
computers
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6. Outline
Introduction
P2P in a
Nutshell
The Evolvable
Agent 1 Introduction
Experimental
Analysis P2P in a Nutshell
Goals
Methodology
The Evolvable Agent
Analysis of
Results
Conclusions 2 Experimental Analysis
Future Works Goals
Methodology
Analysis of Results
3 Conclusions
4 Future Works
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7. The Evolvable Agent Model
Introduction
P2P in a
Nutshell Design principles
The Evolvable
Agent • Agent based approach
Experimental
Analysis
• Fine grain parallelization
Goals • Spatially structured EA
Methodology
Analysis of
Results
• Local selection
Conclusions
Future Works
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8. The Evolvable Agent Model
Introduction
P2P in a
Nutshell Design principles
The Evolvable
Agent • Agent based approach
Experimental
Analysis
• Fine grain parallelization
Goals • Spatially structured EA
Methodology
Analysis of
Results
• Local selection
Conclusions
Future Works
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9. Outline
Introduction
P2P in a
Nutshell
The Evolvable
Agent 1 Introduction
Experimental
Analysis P2P in a Nutshell
Goals
Methodology
The Evolvable Agent
Analysis of
Results
Conclusions 2 Experimental Analysis
Future Works Goals
Methodology
Analysis of Results
3 Conclusions
4 Future Works
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10. Goals and Test-Cases
Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental Goal
Analysis
Goals • Comparison of performances using different population
Methodology
Analysis of
Results
structures
Conclusions
Future Works Ring Watts-Strogatz Newscast
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11. Outline
Introduction
P2P in a
Nutshell
The Evolvable
Agent 1 Introduction
Experimental
Analysis P2P in a Nutshell
Goals
Methodology
The Evolvable Agent
Analysis of
Results
Conclusions 2 Experimental Analysis
Future Works Goals
Methodology
Analysis of Results
3 Conclusions
4 Future Works
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12. Experimental settings
Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental
Analysis • 2-Trap. L=12...60
Goals
Methodology • Population size
Analysis of
Results • Estimated by bisection
Conclusions
• Selectorecombinative
Future Works GA (Mutation less)
• Minimum population
size able to reach 0.98
of SR
• Uniform Crossover
• Binary Tournament
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13. Outline
Introduction
P2P in a
Nutshell
The Evolvable
Agent 1 Introduction
Experimental
Analysis P2P in a Nutshell
Goals
Methodology
The Evolvable Agent
Analysis of
Results
Conclusions 2 Experimental Analysis
Future Works Goals
Methodology
Analysis of Results
3 Conclusions
4 Future Works
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14. Population Structure
Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental Settings
Analysis
Goals
Methodology
Problem instance: 2-trap
Analysis of
Results
Pop. Size: Tuning Algorithm
Conclusions No Mutation
Future Works
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15. Population Structure
Introduction
P2P in a
Nutshell Settings
The Evolvable
Agent
Problem instance: L=60 2-trap
Experimental
Analysis Pop. Size: 135
Goals
Methodology Max. Eval: 5535
Analysis of 1
Results Mutation: Bit-flip Pm = L
Conclusions
Future Works
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16. Conclusions
Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
• Regular lattices require of smaller population sizes
Analysis of
Results ... BUT a bigger number of evaluations to find a solution.
Conclusions
• Different small-world methods produce an equivalent
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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17. Future Works
Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results • Validation of the model in a real P2P infrastructure
Conclusions
• Exploration of other P2P protocols as population
Future Works
structures
• Extension of the P2P concept to other metaheuristics
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18. Questions
Introduction
P2P in a
Nutshell
The Evolvable
Agent
Experimental
Analysis
Goals
Methodology
Analysis of
Results
Conclusions
Thanks for your attention!
Future Works
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