A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5732)


A problem that is inherent to the development and efficient use of solvers is that of tuning parameters. The CP community has a long history of addressing this task automatically. We propose a robust, inherently parallel genetic algorithm for the problem of configuring solvers automatically. In order to cope with the high costs of evaluating the fitness of individuals, we introduce a gender separation whereby we apply different selection pressure on both genders. Experimental results on a selection of SAT solvers show significant performance and robustness gains over the current state-of-the-art in automatic algorithm configuration.


Genetic Algorithm Training Instance Iterate Local Search Parallel Genetic Algorithm Linear Genetic Program 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adenso-Diaz, B., Laguna, M.: Fine-tuning of Algorithms using Fractional Experimental Design and Local Search. Operations Research 54(1), 99–114 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    Birattari, M., Stuetzle, T., Paquete, L., Varrentrapp, K.: A Racing Algorithm for Configuring Metaheuristics. In: GECCO, pp. 11–18 (2002)Google Scholar
  3. 3.
    Coy, S.P., Golden, B.L., Runger, G.C., Wasil, E.A.: Using Experimental Design to Find Effective Parameter Settings for Heuristics. Journal of Heuristics 7(1), 77–97 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Fukunaga, A.: Automated discovery of local search heuristics for satisfiability testing. Evolutionary Computation 16(1), 31–61 (2008)CrossRefGoogle Scholar
  5. 5.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  6. 6.
    Gomes, C., Selman, B.: Algorithm Portfolios. Artificial Intelligence 126(1-2), 43–62 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Huberman, B., Lukose, R., Hogg, T.: An Economics Approach to Hard Computational Problem. Science 265, 51–54 (2003)Google Scholar
  8. 8.
    Hutter, F., Babić, D., Hoos, H.H., Hu, A.J.: Boosting Verification by Automatic Tuning of Decision Procedures. FMCAD, 27–34 (2007)Google Scholar
  9. 9.
    Hutter, F., Hoos, H.H., Stützle, T.: Automatic Algorithm Configuration based on Local Search. In: AAAI, pp. 1152–1157 (2007)Google Scholar
  10. 10.
    Lis, J., Eiben, A.E.: A Multi-Sexual Genetic Algorithm for Multiobjective Optimization. In: IEEE International Conference on Evolutionary Computation, pp. 59–64 (1997)Google Scholar
  11. 11.
    Marinescu, R., Dechter, R.: And/Or Branch-and-Bound for Graphical Models. In: IJCAI, pp. 224–229 (2005)Google Scholar
  12. 12.
    Miller, G.F., Todd, P.M.: The Role of Mate Choice in Biocomputation. Evolution and Biocomputation, 169–204 (1995)Google Scholar
  13. 13.
    Minton, S.: Automatically Configuring Constraint Satisfaction Programs. Constraints 1(1), 1–40 (1996)MathSciNetGoogle Scholar
  14. 14.
    Oltean, M.: Evolving evolutionary algorithms using linear genetic programming. Evolutionary Computation 13(3), 387–410 (2005)CrossRefGoogle Scholar
  15. 15.
    Preuss, M., Bartz-Beielstein, T.: Sequential Parameter Optimization Applied to Self-adaptation for Binary-coded Evolutionary Algorithms. Parameter Setting in Evolutionary Algorithms: Studies in Computational Intelligence, 91–119 (2007)Google Scholar
  16. 16.
    Rejeb, J., AbuElhaij, M.: New Gender Genetic Algorithm for Solving Graph Partitioning Problems. Circuits and Systems 1, 444–446 (2000)Google Scholar
  17. 17.
    Rochat, Y., Taillard, R.D.: Probabilistic Diversification and Intensification in Local Search for Vehicle Routing. Journal of Heuristics 1, 147–167 (1995)CrossRefzbMATHGoogle Scholar
  18. 18.
    Sanchez-Velazco, J., Bullinaria, J.A.: Gendered Selection Strategies in genetic Algorithms for Optimization. UKCI, 217–223 (2003)Google Scholar
  19. 19.
    Vrajitoru, D.: Simulating Gender Separation with Genetic Algorithms. In: GECCO, pp. 634–641 (2002)Google Scholar
  20. 20.
    Wall, M.: GAlib: A C++ Library of Genetic Algorithm Components. MIT, Cambridge (1996), Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Universitat de LleidaSpain
  2. 2.Department of Computer ScienceBrown UniversityProvidenceUSA

Personalised recommendations