Simulating the Evolution of Ant Behaviour in Evaluating Nest Sites

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Simulating the Evolution of Ant Behaviour in
Evaluating Nest Sites

• James Marshall, Tim Kovacs, Anna Dornhaus
• and Nigel Franks

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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?

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Evolutionary Trajectory of Ants

• Two approaches to
• simulation of evolution
• simulate evolution in detail (hard!)
• compare specific alternative behaviours (easier)

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Nest Size Evaluation

• Leptothorax albipennis appears to measure
• nest volume using an algorithm called
• Buffons 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

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Movement of a Real Ant
Figure from Mallon, E.B. and Franks, N.R. Ants
Estimate Area Using Buffons Needle. Proc. R.
Soc. Lond. B 267(2000) 765-770.
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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?

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Experiments

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

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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

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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

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Experimental Details

• Ant converts arousal level at end of visit to
  size classification e using
• e (c-1) -minint(r/d), c-1
• where
• c is number of size categories (3),
• r is arousal level and
• d is classification divisor

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Fitness Calculation

• An ants 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

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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

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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
• Buffons needle can be implemented with 1-pass
• But 1-pass spent no less time in nest
• Perhaps our simulation didnt capture something
  important

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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 cant lay and detect pheromones
  simultaneously
• Weve shown 2-pass isnt necessary
  algorithmically