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Adaptive geographical search algorithms optimized for network topology

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Andrea Wiggins
Andrea Wiggins

The document discusses using an adaptive algorithm to improve geographical search in networks. The algorithm uses agents that combine elements of known search strategies and adjust their weights based on traversal length. The agents traverse randomly generated graphs to find a goal node. Over many trials on diverse network topologies, the agents' strategies evolve to find shorter paths. This adaptive approach could discover search strategies tailored to different graph characteristics.

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Adaptive Geographical Search in Networks Andrea Wiggins EECS 549, Winter 2007
The Problem ,[object Object],[object Object],[object Object],[object Object]
The Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Results 12.4 2.2 6.2 28.6 22.9 28.0 Standard Deviation 9.7 0.9 5.1 49.4 27.2 36.6 Mean Revisits r = 4 Revisits r = 2 Revisits r = 1 Steps r = 4 Steps r = 2 Steps r = 1 10 trials
The Proposal ,[object Object],[object Object],[object Object]
The Simulation ,[object Object],[object Object],[object Object],[object Object]
The Environments ,[object Object],[object Object],[object Object],[object Object]
The Agents ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Interactions ,[object Object],[object Object],[object Object]
The Rules ,[object Object],[object Object],[object Object]
The Outcomes ,[object Object],[object Object],[object Object],[object Object]
Thank you! ,[object Object]

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Adaptive geographical search algorithms optimized for network topology

  • 1. Adaptive Geographical Search in Networks Andrea Wiggins EECS 549, Winter 2007
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  • 4. The Results 12.4 2.2 6.2 28.6 22.9 28.0 Standard Deviation 9.7 0.9 5.1 49.4 27.2 36.6 Mean Revisits r = 4 Revisits r = 2 Revisits r = 1 Steps r = 4 Steps r = 2 Steps r = 1 10 trials
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