A Memetic Approach to Golomb Rulers

  • Carlos Cotta
  • Iván Dotú
  • Antonio J. Fernández
  • Pascal Van Hentenryck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


Finding Golomb rulers is an extremely challenging optimization problem with many practical applications. This problem has been approached by a variety of search methods in recent years. We consider in this work a hybrid evolutionary algorithm that incorporates ideas from greedy randomized adaptive search procedures (GRASP), tabu-based local search methods (TS) and scatter search (SS). In particular, GRASP and TS are embedded into a SS algorithm to serve as initialization and restarting methods for the population and as improvement technique respectively. The resulting memetic algorithm significantly outperforms earlier approaches (including other hybrid EAs, as well as hybridizations of local search and constraint programming), finding optimal rulers where the mentioned techniques failed.


Local Search Tabu Search Combination Method Memetic Algorithm Greedy Randomize Adaptive Search Procedure 
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

  • Carlos Cotta
    • 1
  • Iván Dotú
    • 2
  • Antonio J. Fernández
    • 1
  • Pascal Van Hentenryck
    • 3
  1. 1.Dpto. de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaSpain
  2. 2.Dpto. de Ingeniería InformáticaUniversidad Autónoma de MadridSpain
  3. 3.Brown UniversityProvidence

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