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Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve

Alexandre Quemy 1 Marc Schoenauer 1, 2, * Vincent Vidal 3 Johann Dréo 4 Pierre Savéant 4
* Corresponding author
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
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 DaEYAHSP 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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Submitted on : Tuesday, January 27, 2015 - 2:29:55 AM
Last modification on : Tuesday, April 21, 2020 - 1:05:25 AM
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  • HAL Id : hal-01109776, version 1


Alexandre Quemy, Marc Schoenauer, Vincent Vidal, Johann Dréo, Pierre Savéant. Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve. Learning and Intelligent OptimizatioN - LION 9, Jan 2015, Lille, France. pp.262-267. ⟨hal-01109776⟩



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