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A Study into Ant Colony Optimization, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems

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اقرأ أكثر حفظ في قائمتي
  • المؤلفون: van Hemert, Jano; Solnon, Christine
  • المصدر:
    Evolutionary Computation in Combinatorial Optimization ; 4th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004) ; https://hal.science/hal-01541524 ; 4th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004), May 2004, Coimbra, Portugal. pp.114-123, ⟨10.1007/978-3-540-24652-7_12⟩
  • الموضوع:
    [INFO]Computer Science [cs]
  • نوع التسجيلة:
    conference object
  • اللغة:
    English
  • معلومة اضافية
    • Contributors:
      Centrum voor Wiskunde en Informatica (CWI); Centrum Wiskunde & Informatica (CWI)-Netherlands Organisation for Scientific Research; Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS); Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL); Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)
    • بيانات النشر:
      HAL CCSD
      Springer Verlag
    • الموضوع:
      2004
    • Collection:
      Portail HAL de l'Université Lumière Lyon 2
    • الموضوع:
      Coimbra; Portugal
    • نبذة مختصرة :
      International audience ; We compare two heuristic approaches, evolutionary computation and ant colony optimisation, and a complete tree-search approach, constraint programming, for solving binary constraint satisfaction problems. We experimentally show that, if evolutionary computation is far from being able to compete with the two other approaches, ant colony optimisation nearly always succeeds in finding a solution, so that it can actually compete with constraint programming. The resampling ratio is used to provide insight into heuristic algorithms performances. Regarding efficiency, we show that if constraint programming is the fastest when instances have a low number of variables, ant colony optimisation becomes faster when increasing the number of variables.
    • Relation:
      hal-01541524; https://hal.science/hal-01541524; https://hal.science/hal-01541524/document; https://hal.science/hal-01541524/file/evocop04-pub.pdf
    • الرقم المعرف:
      10.1007/978-3-540-24652-7_12
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.2EBC3301