Post on 28-Aug-2014
description
P2P EVOLUTIONARY ALGORITHMS: A SUITABLE APPROACH FOR
TACKLING LARGE INSTANCES IN HARD OPTIMIZATION PROBLEMS
J.L.J. Laredo1, A.E. Eiben2, M. van Steen2, P.A. Castillo1, A.M. Mora1, J.J. Merelo1
Dept. of Architecture and Computer Technology1
University of Granada
Dept. of Computer Science2
Vrije Universiteit Amsterdam
Scope
First steps to know if talking about P2P
technology applied to EA makes sense
MASSIVE SCALABILITY
Outline
Introduction
Context of the work: Our Model
Experimental Setup
Results
Conclusions
Introduction
Context of the work: Our Model
Experimental Setup
Results
Conclusions
EA
P2P Networks
P2P and dEA issues
Outline
Introduction
Context of the work: Our Model
Experimental Setup
Results
Conclusions
Overall Architecture
Evolvable Agent
Massive Parallelization
Outline
Outline
Introduction
Context of the work: Our Model
Experimental Setup
Results
Conclusions
Introduction
Context of the work: Our Model
Experimental Setup
Results
Conclusions
Outline
Introduction
Context of the work: Our Model
Experimental Setup
Results
Conclusions
Outline
Introduction
EA
P2P Networks
P2P and dEA issues
Bio-inspired optimization methods based on population
1 0 0 1 1 1 1 1 1 1
1 0 1 1 0 0 1 0 0 1
1 0 0 1 1 1 0 1 1 1
1 0 0 0 1 0 0 0 0 0
1 0 0 0 0 0 0 1 1 1
14)(xf
25)(xf
10)(xf
8)(xf
4)(xf
Introduction
EA
P2P Networks
P2P and dEA issues
Selection
Bio-inspired optimization methods based on population
1 0 0 1 1 1 1 1 1 1
1 0 1 1 0 0 1 0 0 1
1 0 0 1 1 1 0 1 1 1
1 0 0 0 1 0 0 0 0 0
1 0 0 0 0 0 0 1 1 1
14)(xf
25)(xf
10)(xf
8)(xf
4)(xf
1 0 0 1 1 1 1 1 1 1
Introduction
Selection
Recombination
Bio-inspired optimization methods based on population
1 0 1 1 0 0 1 0 0 1 14)(xf
25)(xf
1 0 1 1 0 1 1 1 1 1
EA
P2P Networks
P2P and dEA issues
30)(xf
1 0 1 1 0 1 0 1 1 1 29)(xf
Introduction
EA
P2P Networks
P2P and dEA issues
Overlay Network
Physical Network
Introduction
EA
P2P Networks
P2P and dEA issues
• Volunteer Computing
• Convergence P2P GRID
• Application Level Networks
•Dynamic neighborhood
(i.e. Small World)
EA
P2P Networks
P2P and dEA issues
Introduction
P2P = LARGE SCALE
EA Population Size Є (40, 400)
1000s peers are inhabited places
Introduction
EA Population Size Є (40, 400)
Wrong assumption!!!
• Population Size depends on the problem instance complexity
• Larger Instances require larger population sizes
• Practitioners tackle large instances with few individuals due to a
lack in resources
• We have many resources in P2P systems
EA
P2P Networks
P2P and dEA issues
Introduction
The Population Size of a P2P EA should be
determined following a scalability analysis
EA
P2P Networks
P2P and dEA issues
Overall Architecture
Evolvable Agent
Massive Parallelization
Context of the work: Our Model
Population of Evolvable Agents
Population Structure: Gossiping protocol newscast
Evolvable Agent
St Initialize
DO
Sols Selection
St +1 Recombination(Sols,Pc )
Evaluation (St +1)
If St +1 better than St
St St + 1
Overall Architecture
Evolvable Agent
Massive Parallelization
Context of the work: Our Model
Overall Architecture
Evolvable Agent
Massive Parallelization
• Cuadratic number of edges
• High clustering coefficient
• Lineal number of edges
• Low clustering coefficient
• Lineal number of edges
• High clustering coefficient
Context of the work: Our Model
Experimental Setup
Test-bed
PeerSim
Parameters
Population size Bisection, 51200
Crossover DPX, Pc = 1.0
Mutation No mutation
Selection Binary Tournament
PROBLEMS
Instances MMDP, wP-PEAKS, P-PEAKS
GA EvAg Newscast
0.98 of SR
MMDP
Results
WPPEAKS
Results
PPEAKS
Results
Conclusions
Agent-based approach for dEA on P2P
Algorithmically viable, 0.98 SR
Massively scalable
Extra peers don’t decrease the performance
Large instances, large number of resources
Future Works: DOP, Self-adaptive population,
Churn, real environment
THANK YOU!