Divide-and-Evolve: A New Memetic Scheme for Domain-Independent Temporal Planning

  • Marc Schoenauer
  • Pierre Savéant
  • Vincent Vidal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3906)


An original approach, termed Divide-and-Evolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Problems (TPPs). Whereas standard Memetic Algorithms use local search methods to improve the evolutionary solutions, and thus fail when the local method stops working on the complete problem, the Divide-and-Evolve approach splits the problem at hand into several, hopefully easier, sub-problems, and can thus solve globally problems that are intractable when directly fed into deterministic OR algorithms. But the most prominent advantage of the Divide-and-Evolve approach is that it immediately opens up an avenue for multi-objective optimization, even though the OR method that is used is single-objective. Proof of concept approach on the standard (single-objective) Zeno transportation benchmark is given, and a small original multi-objective benchmark is proposed in the same Zeno framework to assess the multi-objective capabilities of the proposed methodology, a breakthrough in Temporal Planning.


Pareto Front Local Algorithm Memetic Algorithm State Invariant Local Search Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marc Schoenauer
    • 1
  • Pierre Savéant
    • 2
  • Vincent Vidal
    • 3
  1. 1.Projet TAO, INRIA Futurs, LRI, Bt. 490Université Paris SudOrsayFrance
  2. 2.Thales Research & Technology France, RD 128PalaiseauFrance
  3. 3.CRIL & Université d’ArtoisLensFrance

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