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Evolutionary search for understanding movement dynamics on mixed networks

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Abstract

This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis.

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Authors and Affiliations

  1. Swarmotics, LLC, Laramie, WY, USA

    William M. Spears

  2. University of Wyoming, Laramie, WY, USA

    Steven D. Prager

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  1. William M. Spears
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  2. Steven D. Prager
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Correspondence to Steven D. Prager.

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Spears, W.M., Prager, S.D. Evolutionary search for understanding movement dynamics on mixed networks. Geoinformatica 17, 353–385 (2013). https://doi.org/10.1007/s10707-012-0155-x

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  • DOI: https://doi.org/10.1007/s10707-012-0155-x

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