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Ontologies to Lead Knowledge Intensive Evolutionary Algorithms: Principles and Case Study

Author

Listed:
  • Carlos Adrian Catania

    (University of Strasbourg, Strasbourg, France)

  • Cecilia Zanni-Merk

    (ICube Laboratory/INSA Strasbourg, Strasbourg, France)

  • François de Bertrand de Beuvron

    (University of Strasbourg, Strasbourg, France)

  • Pierre Collet

    (University of Strasbourg, Strasbourg, France)

Abstract
Evolutionary Algorithms (EA) have proven to be very effective in optimizing intractable problems in many areas. However, real problems including specific constraints are often overlooked by the proposed generic models. The authors' goal here is to show how knowledge engineering techniques can be used to guide the definition of Evolutionary Algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. They propose a methodology based on the structuring of the conceptual model underlying the problem, in the form of a labelled domain ontology suitable for optimization by EA. The case studyfocuses on the logistics involved in the transportation of patients. Although this problem belongs to the well-known family of Vehicle Routing Problems, its specificity comes from the data and constraints (cost, legal and health considerations) that must be taken into account. The precise definition of the knowledge model with thelabelled domain ontology permits the formal description of the chromosome, the fitness functions and the genetic operators.

Suggested Citation

  • Carlos Adrian Catania & Cecilia Zanni-Merk & François de Bertrand de Beuvron & Pierre Collet, 2016. "Ontologies to Lead Knowledge Intensive Evolutionary Algorithms: Principles and Case Study," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 7(1), pages 78-100, January.
    • Handle: RePEc:igg:jkss00:v:7:y:2016:i:1:p:78-100
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