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Research Associate Computing, Engineering & Physical Sciences University of Central Lancashire, UK
Email: jpcartlidge@uclan.ac.uk
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
John Cartlidge: ALife XI, Winchester, UK 3 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 4 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 5 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 6 5th August 2008
Symmetry n Mutation introduces genetic novelty n Symmetric system with unbiased mutation profile
¨ Populations have equal chance of +/– mutation ¨ Neither population has an advantage
John Cartlidge: ALife XI, Winchester, UK 7 5th August 2008
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.
John Cartlidge: ALife XI, Winchester, UK 8 5th August 2008
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?
John Cartlidge: ALife XI, Winchester, UK 9 5th August 2008
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’
John Cartlidge: ALife XI, Winchester, UK 10 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 11 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 12 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 13 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 14 5th August 2008
DV in Action
John Cartlidge: ALife XI, Winchester, UK 15 5th August 2008
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’
John Cartlidge: ALife XI, Winchester, UK 16 5th August 2008
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
John Cartlidge: ALife XI, Winchester, UK 17 5th August 2008
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
jpcartlidge@uclan.ac.uk
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