Copyright © John Cartlidge 2011
Using EStA: The Evolutionary Steering App
EStA is a research and teaching tool that allows users to
“steer” a coevolutionary system during runtime .
Two populations (hosts and parasites) are coevolving. Each generation
individuals play the “Greater Than” game, a simple game where
two players compare the number of 1s in their genetic bitstrings . The
player with the greatest number of 1s is the winner and scores 1 point.
The loser receives nothing. Individuals are selected to breed based on
their total scores against a “sample” of opponents from the
opposing population. At the start, all individuals are initialised with
all bits zeroed. The goal is to evolve “optimal” individuals
containing all (100) bits set to 1.
The “absolute” fitness of every individual (the number of 1s
contained in the genotype) is plotted in the top-right panel. This allows
us to view progress . However, the evolutionary mechanism does
not have direct access to these values. Rather, when
selecting which individuals to breed, evolution must use the scores
calculated by playing the Greater Than game. For each population, the
average of these scores is plotted in the bottom-right panel as
“mean relative fitness”.
Sometimes, all individuals in one population are able to beat (are
“Greater Than”) all individuals in the opposing population.
When this happens, populations are said to have “disengaged”.
Disengagement can affect progress and may stop the discovery of
optimal individuals as the removal of (co)evolutionary pressures
causes the population to randomly “drift” around
genotype space .
Disengagement is more likely to occur when one population has an advantage
over another. You can control the advantage that one population has by
using the parasite bias slider. Values greater than 0.5 favour parasites,
while values less than 0.5 favour hosts. A value of 0.5 produces an
unbiased coevolutionary system, where both populations have an equal 50
chance of flipping a 1 or a 0 each time a mutation event occurs. The host
population mutation bias is always fixed at 0.5. Thus, with no selection
pressure, a host population will naturally drift toward individuals with an
“absolute fitness” of 50 (represented on the
absolute fitness graph as a solid black line). In contrast, the parasite
population will naturally drift toward individuals with an absolute fitness
of 100 x Parasite Bias (represented on the absolute fitness
graph as a black dotted line). Hence, if populations have an absolute fitness
above their respective “drift” lines, it must be due to
coevolutionary competition driving evolution .
“Virulence” can be used to counter the effects of bias . You
can use the sliders below the virulence panels to set the virulence of
each population. Reducing virulence helps to stop disengagement .
However, reducing virulence can also slow evolutionary progress. Selecting an
appropriate virulence is a balancing act. You can see this for yourself
by trying to evolve optimal solutions under different bias levels. See
what happens when you vary bias during a run. Is it harder? The distribution
of individuals' Greater Than scores are plotted as error bars on the
virulence curves of each population. Is this information useful in
guiding your choice of virulence? 
“AVA” (Autonomous Virulence Adaptation) is an automated process
for setting the virulence levels of a population . If you switch AVA on,
you will see virulence automatically vary over time. How does AVA perform?
Are you able to steer the system better than AVA can? To see “under
the hood” of AVA and alter setting, select the AVA tab an
alter the settings.
Finally, why not see the effect that other parameters have on the system?
“Random parasites” is the equivalent of evolution against
a random set of opponents. How does this compare with coevolution?
What is the effect of “elitism”, “noise”, and
a mutation rate that falls over time? [4,5,6]
EStA was first developed at the University of Leeds in 2002 by John
Cartlidge and Martin Thompson. EStA has subsequently been developed and
maintained by John Cartlidge. For full details and further reading on
EStA, and the concept of “steering” a coevolutionary
system, please refer to:
S. Bullock, J. Cartlidge, & M. Thompson, (2002), “Prospects
for computational steering of evolutionary computation,” in
Workshop Proc. 8th Int. Conf. Artif. Life, E. Bilotta et al., Eds.
Sydney, Australia: MIT Press, Dec. 2002, pp. 131–137.
(7 pages, approx. 4,500 words).
Coevolving populations are playing the “Greater Than Game”
(GTG). For full details and further reading on the GTG, please refer to:
R. A. Watson & J. B. Pollack, (2011)
“Coeovlutionary dynamics in a minimal substrate,”
in Proc. Genetic and Evolutionary Computation Conference (GECCO),
L. Spector et al. (eds), Morgan Kauffmann, Jul. 2001, pp. 702-709.
Visualising “progress” in a coevolutionary system is notoriously
difficult. For a further reading, refer to:
J. Cartlidge & S. Bullock, (2004), “Unpicking tartan
CIAO plots: Understanding irregular coevolutionary cycling,”
Adaptive Behaviour, vol. 12, no. 2, pp. 69–92, Jun. 2004.
(24 pages, approx. 14,000 words).
For a comprehensive discussion on disengagement in coevolutionary
systems, refer to:
J. Cartlidge, (2004), “Rules of Engagement:
Competitive Coevolutionary Dynamics in Computational Systems,”
PhD thesis, Sch. Comput., Univ. Leeds, UK.
(210 pages, approx. 75,000 words).
For a discussion on the dynamics of coevolutionary systems, refer to:
J. Cartlidge & S. Bullock, (2003), “Caring versus sharing:
How to maintain engagement and diversity in coevolving populations,”
in Proc. 7th Eur. Conf. Artif. Life (ECAL’03), W. Banzhaf et al., Eds.
Dortmund, Germany: Springer Verlag, Sep. 2003, pp. 299–308.
(10 pages, approx. 5,000 words).
Inspired by virulence in natural coevolutionary systems, the
“Reduced Virulence” technique was first introduced by John
Cartlidge in 2002 to combat disengagement. Refer to:
J. Cartlidge & S. Bullock, (2002), “Learning
lessons from the common cold: How reducing parasite virulence
improves coevolutionary optimization,” in Proc. Congr.
Evol. Comput. (CEC’02), D. Fogel et al., Eds.
Honolulu, HI: IEEE Press, Jun. 2002, pp. 1420–1425.
(7 pages, approx. 4,500 words).
Reduced virulence has been shown to improve coevolutionary optimisation
by reducing the likelihood of disengagement. Refer to:
J. Cartlidge & S. Bullock, (2004), “Combating coevolutionary
disengagement by reducing parasite virulence,” Evol. Comput.,
vol. 12, no. 2, pp. 193–222, Summer 2004.
(30 pages, approx. 15,000 words).
First developed in 2008 by John Cartlidge, AVA is a technique for
dynamically adapting population virulence during
runtime. It has been shown to improve coevolutionary optimisation in a
number of domains. For further reading, refer to the following publications:
J. Cartlidge, (2008), “Dynamically adapting parasite virulence
to combat coevolutionary disengagement (abstract),” in Proc.
11th Int. Conf. Simulation and Synthesis Living Systems (Alife-11),
S. Bullock et al., Eds. Winchester, UK: MIT Press, Aug. 2008, p. 757.
[Presentation With Movies]
(1 page, approx. 500 words);
J. Cartlidge & D. Ait-Boudaoud, (2011), “Autonomous
virulence adaptation improves coevolutionary optimization,”
IEEE Trans. Evol. Comput., vol. 15, no. 2, pp. 215–229, Apr. 2011.
(15 pages, approx. 13,000 words).
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