On the generality of parameter tuning in evolutionary planning

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. Categories and Subject Descriptors I.2.8 [Computing Methodologies]: Artificial Intelligence Problem Solving, Control Methods, and Search General Terms Algorithms Keywords AI Planning, Memetic Algorithms, Parameter Tuning, Racing

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Jacques Bibai, Pierre Savéant, Marc Schoenauer, Vincent Vidal
• Keywords: Artificial Intelligence, Artificial Intelligence Planning, GECCO 2010, Optimization, Parameter