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Evolutionary Multi-objective Optimization for landscape system design

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Abstract

Increasing recognition of the extent and speed of habitat fragmentation and loss, particularly in the urban fringe, is driving the need to analyze qualitatively and quantitatively regional landscape structures in land-use planning and environmental policy implementation. This paper introduces an Evolutionary Multi-objective Optimization (EMO) methodology to estimate the Pareto optimal set of landscape designs generated from a series of underlying ecological principles. The results of applying these principles via EMO to a study site are presented and a hierarchical clustering methodology is introduced to assist in evaluating the population of solutions generated.

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Acknowledgments

The authors would like to acknowledge partial support of the this work from GEOIDE-NCE Grant HSS-SDS-17. Thanks also to Christina Moulton for help in creating the results data set and to the editors and reviewers for helpful suggestions on the presentation of our ideas and results.

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Authors and Affiliations

  1. Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada

    S. A. Roberts

  2. School of Surveying, University of Otago, Dunedin, New Zealand

    G. B. Hall

  3. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

    P. H. Calamai

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  1. S. A. Roberts
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  2. G. B. Hall
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  3. P. H. Calamai
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Correspondence to S. A. Roberts.

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Roberts, S.A., Hall, G.B. & Calamai, P.H. Evolutionary Multi-objective Optimization for landscape system design. J Geogr Syst 13, 299–326 (2011). https://doi.org/10.1007/s10109-010-0136-2

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  • DOI: https://doi.org/10.1007/s10109-010-0136-2

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