Skip to main content
SpringerLink
Log in

Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms

  • Chapter
Advances in Metaheuristics for Hard Optimization

Part of the book series: Natural Computing Series ((NCS))

Abstract

Memetic algorithms are hybridizations of evolutionary algorithms (EAs) with problem- specific heuristics or other meta-heuristics, that are generally used within the EA to locally improve the evolutionary solutions. However, this approach fails when the local method stops working on the complete problem. Divide-and-Evolve is an original approach that evolutionarily builds a sequential slicing of the problem at hand into several, hopefully easier, sub-problems: the embedded (meta-)heuristic is only asked to solve the ‘small’ problems, and Divide-and-Evolve is thus able to globally solve problems that are intractable when directly fed into the heuristic.The Divide and- Evolve approach is described here in the context of temporal planning problems (TPPs), and the results on the standard Zeno transportation benchmarks demonstrate its ability to indeed break the complexity barrier. But an even more prominent advantage of the Divide-and-Evolve approach is that it immediately opens up an avenue for multi-objective optimization, even when using single-objective embedded algorithm

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Bacchus. The 2000 AI Planning Systems Competition. Artificial Intelligence Magazine, 22(3):47–56, 2001

    Google Scholar 

  2. A.H. Brie and P. Morignot. Genetic Planning Using Variable Length Chromosomes. In 15th Int. Conf. on Automated Planning and Scheduling, 2005

    Google Scholar 

  3. T. Bylander. The Computational Complexity of Propositional STRIPS planning. Artificial Intelligence, 69(1-2):165–204, 1994

    Article  MATH  MathSciNet  Google Scholar 

  4. K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization. In M. Schoenauer et al., editor, PPSN’2000, pages 849–858. Springer-Verlag, LNCS 1917, 2000

    Google Scholar 

  5. C. Desquilbet. Détermination de trajets optimaux par algorithmes génétiques. Rapport de stage d’option B2 de l’Ecole Polytechnique. Palaiseau, France, Juin 1992 Advisor: Marc Schoenauer. In French

    Google Scholar 

  6. A.E. Eiben and M. Schoenauer. Evolutionary Computing. Information Processing Letter, 82(1):1–6, 2002

    Article  MATH  MathSciNet  Google Scholar 

  7. A.E. Eiben and J.E. Smith. Introduction to Evolutionary Computing. Springer Verlag, 2003

    Google Scholar 

  8. R. Fikes and N. Nilsson. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial Intelligence, 1:27–120, 1971

    Article  Google Scholar 

  9. M. Fox and D. Long. The Automatic Inference of State Invariants in TIM. Journal of Artificial Intelligence Research, 9:367–421, 1998

    MATH  Google Scholar 

  10. M. Fox and D. Long. PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research, 20:61–124, 2003

    MATH  Google Scholar 

  11. H. Geffner. Perspectives on Artificial Intelligence Planning. In Proc. AAAI-2002, pages 1013–1023, 2002

    Google Scholar 

  12. J. J. Grefenstette. Incorporating Problem Specific Knowledge in Genetic Algorithms. In Davis L., editor, Genetic Algorithms and Simulated Annealing, pages 42–60. Morgan Kaufmann, 1987

    Google Scholar 

  13. W.E. Hart, N. Krasnogor, and J.E.Smith, editors. Evolutionary Computation – Special Issue on Memetic Algorithms, Volume 12:3. MIT Press, 2004

    Google Scholar 

  14. W.E. Hart, N. Krasnogor, and J.E. Smith, editors. Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, Vol. 166. Springer Verlag, 2005

    Google Scholar 

  15. J. Hoffmann, J. Porteous, and L. Sebastia. Ordered Landmarks in Planning. Journal of Artificial Intelligence Research, 22:215–278, 2004

    MATH  MathSciNet  Google Scholar 

  16. Jörg Hoffmann and Stefan Edelkamp. The Deterministic Part of IPC-4: An Overview. Journal of Artificial Intelligence Research, 24:519–579, 2005

    MATH  Google Scholar 

  17. J. Koehler and J. Hoffmann. On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm. Journal of Artificial Intelligence Research, 12:338–386, 2000

    MATH  MathSciNet  Google Scholar 

  18. R. Korf. Planning as Search: A Quantitative Approach. Artificial Intelligence, 33:65–88, 1987

    Article  Google Scholar 

  19. D. Long and M. Fox. The 3rd International Planning Competition: Results and Analysis. Journal of Artificial Intelligence Research, 20:1–59, 2003

    Article  MATH  Google Scholar 

  20. D. McDermott. PDDL – The Planning Domain Definition language. At http://ftp.cs.yale.edu/pub/mcdermott, 1998

    Google Scholar 

  21. D. McDermott. The 1998 AI Planning Systems Competition. Artificial Intelligence Magazine, 21(2):35–56, 2000

    Google Scholar 

  22. P. Merz and B. Freisleben. Fitness Landscapes and Memetic Algorithm Design. In David Corne, Marco Dorigo, and Fred Glover, editors, New Ideas in Optimization, pages 245–260. McGraw-Hill, London, 1999

    Google Scholar 

  23. N. J. Radcliffe and P. D. Surry. Fitness variance of formae and performance prediction. In L. D. Whitley and M. D. Vose, editors, Foundations of Genetic Algorithms 3, pages 51–72. Morgan Kaufmann, 1995

    Google Scholar 

  24. J.L. Schlabach, C.C. Hayes, and D.E. Goldberg. FOX-GA: A Genetic Algorithm for Generating and Analyzing Battlefield Courses of Action. Evolutionary Computation, 7(1):45–68, 1999

    Google Scholar 

  25. Marc Schoenauer, Pierre Savéant, and Vincent Vidal. Divide-and-Evolve: a new memetic scheme for domain-independent temporal planning. In J. Gottlieb and G. Raidl, editors, Proc. EvoCOP’06. Springer Verlag, 2006

    Google Scholar 

  26. D. Smith and D. S. Weld. Temporal Planning with Mutual Exclusion Reasoning. In Proc. IJCAI-99, pages 326–337, 1999

    Google Scholar 

  27. L. Spector. Genetic Programming and AI Planning Systems. In Proc. AAAI 94, pages 1329–1334. AAAI/MIT Press, 1994

    Google Scholar 

  28. V. Vidal and H. Geffner. Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming. In Proc. AAAI-2004, pages 570–577, 2004

    Google Scholar 

  29. V. Vidal and H. Geffner. Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming (to appear). Artificial Intelligence, 2005

    Google Scholar 

  30. D. S. Weld. An Introduction to Least Commitment Planning. AI Magazine, 15(4):27–61, 1994

    Google Scholar 

  31. D. Whitley, T. Starkweather, and D. Fuquay. Scheduling problems and traveling salesman: The genetic edge recombination operator. In J. D. Schaffer, editor, Proc. 3 rd Intl Conf. on Genetic Algorithms. Morgan Kaufmann, 1989

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. INRIA Futurs, Parc Orsay Université, 91893, Orsay, France

    Marc Schoenauer

  2. Thales Research and Technology, Palaiseau, France

    Pierre Savéant

  3. CRIL, IUT de Lens, 62307, Lens, France

    Vincent Vidal

Authors
  1. Marc Schoenauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pierre Savéant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Vidal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Laboratory LiSSi, University of Paris 12, 61 Avenue du Général de Gaulle, 94010, Créteil, France

    Patrick Siarry

  2. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Schoenauer, M., Savéant, P., Vidal, V. (2007). Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms. In: Siarry, P., Michalewicz, Z. (eds) Advances in Metaheuristics for Hard Optimization. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72960-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72960-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72959-4

  • Online ISBN: 978-3-540-72960-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation