Using genetic algorithms for solving hard problems in GIS

S.F. Dijk, van, D. Thierens, M. Berg, de

    Research output: Contribution to journalArticleAcademicpeer-review

    25 Citations (Scopus)


    Genetic algorithms (GAs) are powerful combinatorial optimizers that are able to find close-to-optimal solutions for difficult problems by applying the paradigm of adaptation through Darwinian evolution. We describe a framework for GAs capable of solving certain optimization problems encountered in geographical information systems (GISs). The framework is especially suited for geographical problems since it is able to exploit their geometrical structure with a novel operator called the geometrically local optimizer. Three such problems are presented as case studies: map labeling, generalization while preserving structure, and line simplification. Experiments show that the GAs give good results and are flexible as well.
    Original languageEnglish
    Pages (from-to)381-413
    Issue number4
    Publication statusPublished - 2002


    Dive into the research topics of 'Using genetic algorithms for solving hard problems in GIS'. Together they form a unique fingerprint.

    Cite this