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Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve

  • Alexandre Quemy
  • Marc Schoenauer
  • Vincent Vidal
  • Johann Dréo
  • Pierre Savéant
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8994)

Abstract

A method to generate various size tunable benchmarks for multi-objective AI planning with a known Pareto Front has been recently proposed in order to provide a wide range of Pareto Front shapes and different magnitudes of difficulty. The performance of the Pareto-based multi-objective evolutionary planner DaE \(_{\text {YAHSP}}\) are evaluated on some large instances with singular Pareto Front shapes, and compared to those of the single-objective aggregation-based approach.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexandre Quemy
    • 1
  • Marc Schoenauer
    • 1
  • Vincent Vidal
    • 2
  • Johann Dréo
    • 3
  • Pierre Savéant
    • 3
  1. 1.TAO ProjectINRIA Saclay and LRI Paris-Sud University and CNRSOrsayFrance
  2. 2.ONERA-DCSDToulouseFrance
  3. 3.THALES Research and TechnologyPalaiseauFrance

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