Introduction

Background
Complex
Networks
Newscast
protocol

Model Design    Peer-to-Peer Evolutionary Computation
The Evo...
Scope

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
                  • Status:...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
Introduction

Introduction

Background      EAs: Bio-inspired population based optimization methods
Complex
Networks
Newsc...
Introduction

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Model
Properti...
Introduction

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Model
Properti...
Introduction

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Model
Properti...
Introduction

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Model
Properti...
Introduction

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Model
Properti...
Introduction

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Model
Properti...
Introduction

Introduction

Background
Complex
Networks
Newscast
protocol                       P2P EC
Model Design       ...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
Population Structure as a complex network

Introduction

Background
Complex
Networks
Newscast
                Watts-Stroga...
Population Structure as a complex network

Introduction
                        Panmictic    Small-world   Regular lattice...
Population Structure as a complex network

Introduction
                        Panmictic    Small-world   Regular lattice...
Population Structure as a complex network

Introduction
                        Panmictic    Small-world   Regular lattice...
Population Structure as a complex network

Introduction
                        Panmictic    Small-world   Regular lattice...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
Newscast

Introduction

Background      Basic Working Principles
Complex
Networks          • Decentralized P2P protocol
Ne...
Newscast

Introduction

Background      Basic Working Principles
Complex
Networks          • Decentralized P2P protocol
Ne...
Newscast: Bootstrapping and Convergence

Introduction

Background
Complex
Networks
                Experiment
Newscast
pro...
Newscast: Robustness

Introduction

Background
Complex
Networks
                Experiment
Newscast
protocol
             ...
Newscast: Scalability

Introduction

Background
Complex
Networks
                Experiment
Newscast
protocol          • S...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
The Evolvable Agent Model

Introduction

Background      Design principles
Complex
Networks          •   Agent based appro...
The Evolvable Agent Model

Introduction

Background      Design principles
Complex
Networks          •   Agent based appro...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
Multi-threading performance on a local computer

Introduction

Background      Experiment
Complex
Networks
Newscast
      ...
Parallel performance on a P2P infrastructure

Introduction

Background      Experiment
Complex
Networks
Newscast
         ...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
Goals and Test-Cases

Introduction

Background
Complex
Networks
                Goals
Newscast
protocol          1 Scalabi...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
Generalised l-trap function

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design      • l-trap funct...
Experimental settings

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
                  • Popul...
Outline

Introduction
                1   Introduction
Background
Complex
Networks
                2   Background
Newscast...
Test-Case 1: Scalability

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Mo...
Test-Case 1: Scalability

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Design
The Evo...
Test-Case 1: Scalability

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Design
The Evo...
Test-Case 1: Scalability

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Design
The Evo...
Test-Case 1: Scalability

Introduction

Background
Complex
Networks
Newscast
protocol
                Larger instance 4-Tr...
Test-Case 1: Scalability

Introduction

Background      Settings
Complex
Networks
Newscast
                Equally paramet...
Test-Case 2: Population Structure

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable...
Test-Case 2: Population Structure

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Desig...
Test-Case 2: Population Structure

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Desig...
Test-Case 2: Population Structure

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Desig...
Test-Case 2: Population Structure

Introduction

Background      Settings
Complex
Networks        Equally parameterized ap...
Test-Case 3: Fault-tolerance

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agen...
Test-Case 3: Fault-tolerance

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agen...
Test-Case 3: Fault-tolerance

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
                  ...
Test-Case 3: Fault-tolerance

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Design
The...
Test-Case 3: Fault-tolerance

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Design
The...
Test-Case 3: Fault-tolerance

Introduction

Background
Complex
Networks
Newscast
protocol        Settings
Model Design
The...
Test-Case 3: Fault-tolerance

Introduction

Background      Settings
Complex
Networks
Newscast        Equally parameterize...
Conclusions

Introduction

Background      Selected publications
Complex         Peer reviewed journal papers :
Networks
N...
Questions

Introduction

Background
Complex
Networks
Newscast
protocol

Model Design
The Evolvable
Agent
Model
Properties
...
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P2P EC: A study of viability

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P2P EC: A study of viability

  1. 1. Introduction Background Complex Networks Newscast protocol Model Design Peer-to-Peer Evolutionary Computation The Evolvable Agent Model A Study of Viability Properties Experimental Analysis Goals Methodology Juan Luis Jim´nez Laredo e Analysis of Results Test-Case 1 Dpto. Arquitectura y Tecnolog´ de Computadores ıa Test-Case 2 Test-Case 3 Universidad de Granada Conclusions 27 de Mayo 2010 1 / 44
  2. 2. Scope Introduction Background Complex Networks Newscast protocol Model Design The Evolvable • Status: Peer-to-Peer Evolutionary Computation (P2P EC) Agent Model represents a parallel solution for hard problems Properties Experimental optimization Analysis Goals • Objective: Find empirical evidences showing the viability Methodology Analysis of of the P2P EC paradigm Results Test-Case 1 Test-Case 2 • Modelling: Fine grained parallel EA using a P2P protocol Test-Case 3 as underlying population structure Conclusions 2 / 44
  3. 3. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 3 / 44
  4. 4. Introduction Introduction Background EAs: Bio-inspired population based optimization methods Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 4 / 44
  5. 5. Introduction Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 5 / 44
  6. 6. Introduction Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 5 / 44
  7. 7. Introduction Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 5 / 44
  8. 8. Introduction Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 6 / 44
  9. 9. Introduction Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 6 / 44
  10. 10. Introduction Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 6 / 44
  11. 11. Introduction Introduction Background Complex Networks Newscast protocol P2P EC Model Design • Virtualization: The Evolvable Agent Model Single view at Properties application level Experimental Analysis • Decentralization: Goals Methodology Analysis of No central Results Test-Case 1 management Test-Case 2 Test-Case 3 • Massive Scalability: Conclusions Up to thousands of computers 7 / 44
  12. 12. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 8 / 44
  13. 13. Population Structure as a complex network Introduction Background Complex Networks Newscast Watts-Strogatz Model protocol • Easy model for constructing small-world networks Model Design The Evolvable • Begins with a ring Agent Model • Rewired edges at random with a probability p Properties Experimental Analysis Goals Methodology p = 0: Ring p = 0.2: Small-world p = 1: Random Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 9 / 44
  14. 14. Population Structure as a complex network Introduction Panmictic Small-world Regular lattice Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 10 / 44
  15. 15. Population Structure as a complex network Introduction Panmictic Small-world Regular lattice Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 10 / 44
  16. 16. Population Structure as a complex network Introduction Panmictic Small-world Regular lattice Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 10 / 44
  17. 17. Population Structure as a complex network Introduction Panmictic Small-world Regular lattice Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model n(n−1) Properties 2 log(n) n Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 10 / 44
  18. 18. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 11 / 44
  19. 19. Newscast Introduction Background Basic Working Principles Complex Networks • Decentralized P2P protocol Newscast protocol • Every node has a cache acting as a routing table Model Design • Dynamical self-organized network The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions Jelasity,02 12 / 44
  20. 20. Newscast Introduction Background Basic Working Principles Complex Networks • Decentralized P2P protocol Newscast protocol • Every node has a cache acting as a routing table Model Design • Dynamical self-organized network The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions Jelasity,02 12 / 44
  21. 21. Newscast: Bootstrapping and Convergence Introduction Background Complex Networks Experiment Newscast protocol • Different network initializations: Watts-Strogatz and Random Model Design • Network characterization: Average path length, Clustering coefficient The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions Jelasity,02 13 / 44
  22. 22. Newscast: Robustness Introduction Background Complex Networks Experiment Newscast protocol • System degradation: Up to 100%. Newscast, Random graph Model Design • Network characterization: Size of largest cluster, Number of partitions The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions Jelasity,02 14 / 44
  23. 23. Newscast: Scalability Introduction Background Complex Networks Experiment Newscast protocol • System traffic: Sizes of networks 1000 and 10000 Model Design • Network characterization: Probability of requests to a node The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions Jelasity,02 15 / 44
  24. 24. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 16 / 44
  25. 25. The Evolvable Agent Model Introduction Background Design principles Complex Networks • Agent based approach Newscast protocol • Fine grain parallelization Model Design The Evolvable • Spatially structured EA Agent Model • Local selection Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 17 / 44
  26. 26. The Evolvable Agent Model Introduction Background Design principles Complex Networks • Agent based approach Newscast protocol • Fine grain parallelization Model Design The Evolvable • Spatially structured EA Agent Model • Local selection Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 17 / 44
  27. 27. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 18 / 44
  28. 28. Multi-threading performance on a local computer Introduction Background Experiment Complex Networks Newscast • Scalability of virtual nodes running in a SMP desktop computer protocol • Fitness evaluation cost Tf ∈ [0.01 . . . 1] seconds Model Design The Evolvable • Test-bed: 1 processor machine and a dual-core processor machine Agent Model Properties Experimental Analysis Goals Methodology T hroughputEA = evaluations time Analysis of Results Test-Case 1 T hroughput Test-Case 2 Speedup = T hroughput EvAg Test-Case 3 sequential Conclusions T imesequential Speedup = T imeEvAg Linear speedup up to the number of processors 19 / 44
  29. 29. Parallel performance on a P2P infrastructure Introduction Background Experiment Complex Networks Newscast • Scalability for a evaluation cost of Lζ=[1.5,2,3] protocol • L ∈ [1 . . . 100] Model Design The Evolvable • N = L2 then N ∈ [1 . . . 10000] Agent Model Properties Experimental Analysis Goals Methodology Analysis of N Tf Results Speedup = Tp Test-Case 1 Test-Case 2 Test-Case 3 Tp = Tf + Tcomm + Tlat Conclusions Gagn´, 03 e Linear speedups for demanding evaluation functions 20 / 44
  30. 30. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 21 / 44
  31. 31. Goals and Test-Cases Introduction Background Complex Networks Goals Newscast protocol 1 Scalability: Suitability for tackling large problem Model Design instances. The Evolvable Agent Model 2 Fault-tolerance: Suitability for tolerating the system Properties Experimental degradation. Analysis Goals Methodology Analysis of Test-Cases Results Test-Case 1 • Test-Case 1: Scalability against canonical approaches in Test-Case 2 Test-Case 3 failure-free environments Conclusions • Test-Case 2: Scalability against other population structures in failure-free environments • Test-Case 3: Fault-tolerance of the model under churn 22 / 44
  32. 32. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 23 / 44
  33. 33. Generalised l-trap function Introduction Background Complex Networks Newscast protocol Model Design • l-trap function (Ackley, The Evolvable Agent 1987): Model Properties • 2-trap: not-deceptive Experimental • 3-trap: partially Analysis Goals deceptive Methodology Analysis of • 4-trap: fully deceptive Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions • L = 12 . . . 60 24 / 44
  34. 34. Experimental settings Introduction Background Complex Networks Newscast protocol Model Design • Population size The Evolvable Agent • Estimated by bisection Model Properties • Selectorecombinative Experimental GA (Mutation less) Analysis • Minimum population Goals Methodology size able to reach 0.98 Analysis of Results of SR Test-Case 1 Test-Case 2 Test-Case 3 • Uniform Crossover Conclusions • Binary Tournament 25 / 44
  35. 35. Outline Introduction 1 Introduction Background Complex Networks 2 Background Newscast protocol Complex Networks Model Design Newscast protocol The Evolvable Agent Model 3 Model Design Properties Experimental The Evolvable Agent Analysis Model Properties Goals Methodology Analysis of 4 Experimental Analysis Results Test-Case 1 Goals Test-Case 2 Test-Case 3 Methodology Conclusions Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 5 Conclusions 26 / 44
  36. 36. Test-Case 1: Scalability Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties • Failure-free environment Experimental Analysis • Canonical approaches: SSGA, GGA Goals Methodology • Metrics: Population Size, Evaluations Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 27 / 44
  37. 37. Test-Case 1: Scalability Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 2-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 28 / 44
  38. 38. Test-Case 1: Scalability Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 3-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 29 / 44
  39. 39. Test-Case 1: Scalability Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 4-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 30 / 44
  40. 40. Test-Case 1: Scalability Introduction Background Complex Networks Newscast protocol Larger instance 4-Trap: L=36 Model Design The Evolvable Agent Pop. Size: 600 Model Properties Max. Eval: 393000 Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 30 / 44
  41. 41. Test-Case 1: Scalability Introduction Background Settings Complex Networks Newscast Equally parameterized SSGA, GGA and EvAg protocol Problem instance: L=36 4-trap Model Design The Evolvable Agent Pop. Size: 600 Model Properties Max. Eval: 393000 1 Experimental Mutation: Bit-flip Pm = L Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 31 / 44
  42. 42. Test-Case 2: Population Structure Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Ring Watts-Strogatz Newscast Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 32 / 44
  43. 43. Test-Case 2: Population Structure Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 2-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 33 / 44
  44. 44. Test-Case 2: Population Structure Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 3-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 34 / 44
  45. 45. Test-Case 2: Population Structure Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 4-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 35 / 44
  46. 46. Test-Case 2: Population Structure Introduction Background Settings Complex Networks Equally parameterized approaches using different pop. Newscast protocol structures Model Design The Evolvable Problem instance: L=36 4-trap Agent Model Pop. Size: 600 Properties Experimental Max. Eval: 393000 1 Analysis Goals Mutation: Bit-flip Pm = L Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 36 / 44
  47. 47. Test-Case 3: Fault-tolerance Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 37 / 44
  48. 48. Test-Case 3: Fault-tolerance Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 37 / 44
  49. 49. Test-Case 3: Fault-tolerance Introduction Background Complex Networks Newscast protocol Model Design • Stutzbach and Rajaie, The Evolvable Agent 2006 Model Properties Experimental • Weibull distribution: Analysis 1 Goals • X = λ(−ln(U )) k Methodology Analysis of Results Test-Case 1 • Shape: k = 0.4 Test-Case 2 Test-Case 3 Conclusions • Scale: λ = 400, 2500 38 / 44
  50. 50. Test-Case 3: Fault-tolerance Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 2-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 39 / 44
  51. 51. Test-Case 3: Fault-tolerance Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 3-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 40 / 44
  52. 52. Test-Case 3: Fault-tolerance Introduction Background Complex Networks Newscast protocol Settings Model Design The Evolvable Problem instance: 4-trap Agent Model Pop. Size: Tuning Algorithm Properties Experimental No Mutation Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 41 / 44
  53. 53. Test-Case 3: Fault-tolerance Introduction Background Settings Complex Networks Newscast Equally parameterized approaches with and without churn protocol Model Design Problem instance: L=36 4-trap The Evolvable Agent Pop. Size: 600 Model Properties Max. Eval: 393000 1 Experimental Mutation: Bit-flip Pm = L Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 42 / 44
  54. 54. Conclusions Introduction Background Selected publications Complex Peer reviewed journal papers : Networks Newscast protocol 1 J.L.J. Laredo, A.E. Eiben, M. van Steen, and J.J. Merelo. Evag: A scalable peer-to-peer evolutionary algorithm. GPEM, 2010. Model Design http://dx.doi.org/10.1007/s10710-009-9096-z. The Evolvable Agent 2 J.L.J. Laredo, P.A. Castillo, A.M. Mora, J.J. Merelo, and C. Fernandes. Model Resilience to churn of a peer-to-peer evolutionary algorithm. IJHPSA, Properties 1(4):260-268, 2009. Experimental 3 J.L.J. Laredo, P.A. Castillo, A.M. Mora, and J.J. Merelo. Evolvable agents, a Analysis fine grained approach for distributed evolutionary computing. Soft Goals Computing, 12(12):1145-1156, 2008. Methodology Peer reviewed conference papers and book chapters : Analysis of Results Test-Case 1 1 J.L.J. Laredo, P.A. Castillo, A.M. Mora, J.J. Merelo, A.C. Rosa, and C. Test-Case 2 Fernandes. Evolvable agents in static and dynamic optimization problems. In Test-Case 3 PPSN X, pages 488-497. Springer, 2008 Conclusions 2 J.L.J. Laredo, A.E. Eiben, M. van Steen, P.A. Castillo, A.M. Mora, and J.J. Merelo. P2P evolutionary algorithms: A suitable approach for tackling large instances in hard optimization problems. In Euro-Par’ 08, pages 622-631. Springer, 2008. 3 J.L.J. Laredo, P.A. Castillo, A.M. Mora, and J.J. Merelo. Exploring population structures for locally concurrent and massively parallel evolutionary algorithms. In IEEE WCCI2008 Proceedings, pages 2610-2617. IEEE Press, Hong Kong, June 2008. 43 / 44
  55. 55. Questions Introduction Background Complex Networks Newscast protocol Model Design The Evolvable Agent Model Properties Experimental Thanks for your attention! Analysis Goals Methodology Analysis of Results Test-Case 1 Test-Case 2 Test-Case 3 Conclusions 44 / 44

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