dynamically adapting parasite virulence to combat ...€¦ · 5th august 2008 john cartlidge: alife...

Research Associate Computing, Engineering & Physical Sciences University of Central Lancashire, UK

Email: [email protected]

Dr John Cartlidge

5th August 2008 John Cartlidge: ALife XI, Winchester, UK 1

Dynamically adapting parasite virulence to combat coevolutionary disengagement

John Cartlidge: ALife XI, Winchester, UK 2 5th August 2008

Synopsis

n Disengagement in coevolutionary systems n Review Reduced Virulence (RV) n Analysis of RV in Counting Ones domain n Present Dynamic Virulence (DV), a novel

method for adapting Virulence online n Summary/Conclusions


Disengagement n  Competitive Coevolutionary Systems

¨ Relative fitness assessment through self-play ¨ Fitness varies as opponents vary in ability

n  Relativity leads to Disengagement ¨ Occurs when one population gets the “upper hand” ¨ Can’t discriminate individuals ∴ no selection pressure

n  Occurs when competitors are badly matched ¨ Suits of armour and nuclear weapons ¨ There must be no outright winner


Reduced Virulence (RV) n Cartlidge, J. & Bullock, S. (2002, 2004) n Reward competitors that sometimes lose

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Score, x

Fitn

ess,

f(x,

v)10.750.5

RV Fitness Transform f(x,v) = 2x ⁄ v – x2⁄ v2

virulence: 0.5 ≤ v ≤ 1.0 relative score: x


RV: An illustrative Example n  Selection only. No mutation. Linear fitness ranking n  Population B has an innovation (20) not found in A n  Trade-off between engagement and innovation loss

V = 1 (standard) V = 0.75 V = 0.5

Selection drives pop B to 20 causing disengagement

Pop B drops genotype 20 and remains engaged at 19

Lots of innovation loss as populations move to 12


Symmetry n  Mutation introduces genetic novelty n  Symmetric system with unbiased mutation profile

¨  Populations have equal chance of +/– mutation ¨  Neither population has an advantage


Asymmetry n  Here population B has a favourable mutation bias

¨  A finds it harder to discover +ve/beneficial genetic innovations

n  Disengagement is exacerbated by asymmetry ¨  In genetic representations, genotype-phenotype mappings, genetic

operators, interaction rules, location in genotype space, etc.


Couting Ones

n Watson & Pollack, GECCO 2001 n Two populations of binary strings n Goal: evolve as many 1s as possible n Asymmetrical bias controlled by varying

mutation bias of one population (parasites) n When is it best to reduce virulence?


Virulence ‘Sweet-Spot’ n  Low bias requires high virulence for both populations n  As bias increases, want progressively lower parasite V

Para

site

viru

lenc

e

Host virulence

Parasite Bias / Asymmetry 0.5 0.6 0.7 0.8 0.9 1.0

Maximums

Engagement

‘Sweet-Spot’


Choosing RV Value

n Problem: ¨ How do we know a priori what the asymmetry

is likely to be? ¨ Is asymmetry is likely to remain fixed?

n Solution: ¨ Adapt virulence dynamically during runtime


Dynamic Virulence (DV) n  Reinforcement learning approach:

¨  Value(t+1) ← Value(t) + LearningRate [Target(t) – Value(t)] n  Each generation, t, update virulence, Vt

¨  ∆Vt = ρ(1 − Xt ⁄φ) (1) n  Xt: Mean relative score of population at time t n  φ: Target mean relative score of population n  ρ: Acceleration (rate of change of virulence)

¨  Μt = µΜt-1 + (1−µ)∆Vt (2) n  µ: Momentum, Μ0 = V0

¨  if µ = 0, then ∀t, Μt = ∆Vt ∴ no momentum ¨  if µ = 1, then ∀t, Μt = V0 ∴ fixed virulence

¨  Vt+1 = Vt+Μt (3) n  0 ≤ φ, ρ, µ ≤ 1


Evolving φ, ρ, µ Acceleration Rate, ρ

00.10.20.30.40.50.60.70.80.91

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Generation

Momentum, µ

00.10.20.30.40.50.60.70.80.91

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Generation

Target Fitness, φ

00.10.20.30.40.50.60.70.80.91

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28Generation

30 runs. Mean value of parameter in population each generation. Bias fixed for each evaluation

Momentum, µ

00.1

0.20.3

0.40.50.6

0.70.8

0.91

0 10 20 30 40 50 60 70

Generation

15 runs. Mean value of parameter in population each generation. Bias varying during each evaluation

Acceleration Rate, ρ

00.1

0.20.3

0.40.5

0.60.7

0.80.9

1

0 10 20 30 40 50 60 70

Generation

Target Fitness, φ

00.1

0.20.3

0.40.5

0.60.7

0.80.9

1

0 10 20 30 40 50 60 70

Generation


DV Performance n  Performance of DV in the Counting Ones domain n  DV Parameters: φ = 0.56; ρ = 0.0125; µ = 0.3

¨  180/180 successful runs. 31/135,000 disengaged generations n  Compare with maximum virulence

¨  79/180 successful runs. 68,900 disengaged generations

Successful runs using fixed virulence (total 180 runs)

0.5 0.6 0.7 0.8 0.9 1.0 Parasite Bias / Asymmetry

0.5 0.6 0.7 0.8 0.9 1.0 Parasite Bias / Asymmetry

Fixed Virulence Fixed Virulence Dynamic Virulence


DV in Action


Lessons for epidemiology? n  Can we use DV for modelling virulence in natural systems? n  Can we translate ideas of RV to the natural world for

control of infectious diseases? ¨  Rather than attack parasites and encourage an arms-race, creating

‘super-bugs’, can we take a reduced virulence approach? ¨  E.g.: ‘Scientists create GM mosquitoes to fight malaria and save

thousands of lives’ (Guardian 2005) n  ‘Plan to breed and sterilize millions of male insects’ n  Project ‘almost ready for testing in wild’


Summary / Conclusions n  Disengagement is problematic and is exacerbated by

asymmetry n  Reducing virulence helps to promote engagement n  As asymmetry increases, virulence should fall n  Its hard to know a priori what virulence level to set n  DV is able to adapt virulence during evolution to find the

best value n  DV has been shown to vastly outperform fixed virulence

(and standard virulence) in the Counting Ones domain


Further Reading n  Cartlidge & Bullock (2002) CEC, p.1420, IEEE Press n  Cartlidge & Bullock (2003) ECAL, p.299, Springer Verlag n  Cartlidge & Bullock (2004) Evolutionary Comp., 12, p.193 n  Cartlidge (2004) PhD Thesis, University of Leeds

Dr John Cartlidge, Research Associate University of Central Lancashire

[email protected]

dynamically adapting parasite virulence to combat ...€¦ · 5th august 2008 john cartlidge: alife...

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