On the Benefit of Sub-Optimality within the Divide-and-Evolve Scheme

Jacques Bibai (1, 2) , Pierre Savéant (1) , Marc Schoenauer (2) , Vidal Vincent (3)

1 Thales Research and Technology [Palaiseau]
2 TAO - Machine Learning and Optimisation
3 ONERA - The French Aerospace Lab [Palaiseau]

Divide-and-Evolve (DaE) is an original “memeticization” of Evolutionary Computation and Artificial Intelligence Planning. DaE optimizes either the number of actions, or the total cost of actions, or the total makespan, by generating ordered sequences of intermediate goals via artificial evolution. The evolutionary part of DaE is based on the Evolving Objects (EO) library, and can theorically use any embedded planner. However, since the introduction of this approach only one embedded planner has been used: the temporal optimal planner CPT. In this paper, we built a new version of DaE based on time-based Atom Choice and we embarked another planner (the sub-optimal planner YAHSP) in order to test the technical robustness of the approach and to compare the impact of using an optimal planner versus using a sub-optimal planner for all kinds of planning problems.

Mots clés: Artificial Intelligence Planning; Evolutionary Computation; Divide-and-Evolve

Domaines: Recherche opérationnelle [math.OC]

Jacques Bibai, Pierre Savéant, Marc Schoenauer, Vidal Vincent. On the Benefit of Sub-Optimality within the Divide-and-Evolve Scheme. 10th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2010 ), Apr 2010, Istanbul, Turkey. pp.23-34, ⟨10.1007/978-3-642-12139-5_3⟩. ⟨inria-00443984⟩