1. Simulating the Evolution of Ant Behaviour in Evaluating Nest Sites
James Marshall, Tim Kovacs, Anna Dornhaus
and Nigel Franks

2. Purpose of Work
Use an Evolutionary Algorithm to study evolution of behaviour in real ants
Note: we use EA to optimise our simulation, not to simulate real evolution
Why did ants evolve this way?
Are other behaviours just as good / better?
What algorithms do ants use to self-organise?

3. Evolutionary Trajectory of Ants
Two approaches to
simulation of evolution:
simulate evolution in detail (hard!)
compare specific alternative behaviours (easier)

4. Nest Size Evaluation Leptothorax albipennis appears to measure
nest volume using an algorithm called
Buffon’s needle
Each ant makes 2 visits to potential nest:
- Visit 1: lay pheromone trail
- Visit 2: assess pheromone density
Between visits ant returns to old nest site

5. Movement of a Real Ant
Figure from: Mallon, E.B. and Franks, N.R. Ants Estimate Area
Using Buffon’s Needle. Proc. R. Soc. Lond. B 267(2000)

6. 1-pass vs 2-pass Evaluation of Nest Size
Why visit potential nest twice?
Nest relocation often occurs in response to attacks and is time-critical
Why not do both steps during same visit?

7. Experiments Simulate nest size evaluation and compare
1-pass and 2-pass strategies.
Most importantly:
does 1-pass work?
i.e. can we implement Buffon’s needle with it?
Also:
which classifies nest size more reliably?
which requires less time in nest?

8. Experimental Details Assumptions:
Ant movement is a constrained random walk
Ants measure pheromone density using an arousal level
increase level when trail crossed
decrease level on all time steps

9. Experimental Details 3 sizes of square nest used
Half of ants use 1-pass and half use 2-pass
Ants evolve the following characteristics:
total time spent in nest t
arousal decay rate
classification divisor d

10. Experimental Details
Ant converts arousal level at end of visit to size classification e using
e = (c-1) -min[int(r/d), c-1]
where
c is number of size categories (3),
r is arousal level and
d is classification divisor

11. Fitness Calculation An ant’s fitness is given by: f = -q |e - s| - t
where
q = 1000 provides selective pressure
towards assessment quality
s is actual nest size and
t is time spent in nest

12. Evolutionary Algorithm
Rank population by fitness
cull least fit 1/3 of population
replace with offspring from fittest 2/3
Generate offspring:
Apply uniform crossover to adjacently ranked pairs of ants with probability 10%
Mutate with probability 1%: change value uniformly at random by up to 10%

13. Results No significant difference between 1 and 2- pass in:
number of trials which reached 100% accuracy
number of generations needed to reach it
total time spent in nest
Conclusion:
Buffon’s needle can be implemented with 1-pass
But 1-pass spent no less time in nest
Perhaps our simulation didn’t capture something important

14. Discussion
Note that 2-pass requires time to make an extra trip to potential nest, so 1-pass is faster
Why do ants use 2-pass?
Evolutionary accident?
Maybe ants can’t lay and detect pheromones simultaneously
We’ve shown 2-pass isn’t necessary algorithmically