Multi-agent Communication Disorders: Dynamic Breeding Networks in Genetic Algorithms Forrest Sondahl William Rand [email protected][email protected]July 14, 2007 Northwestern Institute on Complex Systems http://www.northwestern.edu/nico/ Center for Connected Learning and Computer-Based Modeling http://ccl.northwestern.edu/
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Multi-agent Communication Disorders: Dynamic Breeding ... · Multi-agent Communication Disorders: Dynamic Breeding Networks in Genetic Algorithms Slide 33 / 42 Experiment 2 Vary the
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Multi-agent Communication Disorders: Dynamic Breeding Networks in Genetic Algorithms
Better “more innovative” policies diffuse through the social network, as individuals adopt those policies.
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Diffusion of Innovation
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Diffusion of Innovation
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Diffusion of Innovation
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Diffusion of Innovation
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Complexifications
Policies could be multi-faceted
Agents could take pieces of policies from other agents
Adoption shouldn't be deterministic
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A Model of Diffusion
Each person may:
Keep their own policyCopycat a neighbor's policy
Combine two policies
Slightly change their policy
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A Genetic Model of Diffusion
Each person may:
Keep their own policyCopycat a neighbor's policy
Combine two policies
Slightly change their policy
} Cloning
Crossover
Mutation
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Bringing it together
Our model can be viewed from multiple perspectives.
Hopefully it captures generic aspects of information dispersal in the context of
solving some problem.
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Outline
Genetic algorithm modelDiffusion of innovation modelShow and tellExperimentResultsFuture work
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Network Topologies
Spatial (fixed):Breeding neighborhood defined by “in-radius”
Spatial (dynamic):The agents move in the world
Random (fixed):Erdös-Renyi random graphs
Random (dynamic):Network “rewired” each generation.
Model Demo
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Outline
Genetic algorithm modelDiffusion of innovation modelShow and tellExperimentResultsFuture work
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What's the “problem”?
We used hyperplane-defined functions (HDFs).
Goal: produce a certain pattern of bits. ...*****11100**00101********...
In the fitness function:some sub-patterns are rewarded (schemata)some sub-patterns are penalized (pot-holes)
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Constant parameters
Population size: 256Crossover rate: 0.7Mutation rate: 1 / [ 2 x length_of_bitstring ]Tournament selection with tournament size 3
“Spatial dynamic” specific parameterswiggle-angle amount = between -15 and 15 degreesforward-step amount = 1% of world diagonal.
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Outline
Genetic algorithm modelDiffusion of innovation modelShow and tellExperimentResultsFuture work
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Experiment 1
Vary the network density from 0% to 100%Run the model until a “perfect” solution is found.Measure how many generations it took.(Give up after 3000 generations.)
We ran 60 repetitions for each network density, and present the average.
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Observations
The genetic algorithm is robust, even for sparse networks (≤ 5% density).
We can't see much else.
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Experiment 2
Vary the network density from 0% to 5%Run the model until a “perfect” solution is found.Measure how many generations it took.(Give up after 3000 generations.)
We ran 60 repetitions for each network density, and present the average.