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On the Generality of Parameter Tuning in Evolutionary Planning

Jacques Bibai 1, 2 Pierre Savéant 1 Marc Schoenauer 2, 3 Vincent Vidal 4
2 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 : Divide-and-Evolve (DaE) is an original “memeticization” of Evolutionary Computation and Artificial Intelligence Planning. However, like any Evolutionary Algorithm, DaE has several parameters that need to be tuned, and the already excellent experimental results demonstrated by DaE on benchmarks from the International Planning Competition, at the level of those of standard AI planners, have been obtained with parameters that had been tuned once and for-all using the Racing method. This paper demonstrates that more specific parameter tuning (e.g. at the domain level or even at the instance level) can further improve DaE results, and discusses the trade-off between the gain in quality of the resulting plans and the overhead in terms of computational cost.
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Submitted on : Friday, March 12, 2010 - 11:16:18 AM
Last modification on : Thursday, July 8, 2021 - 3:47:45 AM
Long-term archiving on: : Friday, June 18, 2010 - 9:17:18 PM


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  • HAL Id : inria-00463437, version 1



Jacques Bibai, Pierre Savéant, Marc Schoenauer, Vincent Vidal. On the Generality of Parameter Tuning in Evolutionary Planning. ACM Genetic and Evolutionary Computation Conference (GECCO-2010), Jul 2010, Portland, Oregon, United States. pp.241-248. ⟨inria-00463437⟩



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