A Selecto-recombinative Genetic Algorithm with Continuous Chromosome Reconfiguration

  • Jiří Kubalík
  • Petr Pošík
  • Jan Herold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


A good performance of traditional genetic algorithm is determined by its ability to identify building blocks and grow them to larger ones. To attain this objective a properly arranged chromosome is needed to ensure that building blocks will survive the application of recombination operators. The proposed algorithm periodically rearranges the order of genes in the chromosome while the actual information about the inter-gene dependencies is calculated on-line through the run. Standard 2-point crossover, operating on the adapted chromosomal structure, is used to generate new solutions. Experimental results show that this algorithm is able to solve separable problems with strong intra building block dependencies among genes as well as the hierarchical problems.


Genetic Algorithm Linkage Group Chromosome Structure Critical Heat Flux Fitness Evaluation 
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  1. 1.
    Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis and first results. Complex Systems 3(5), 493–530 (1989)MATHMathSciNetGoogle Scholar
  2. 2.
    Harik, G., Goldberg, D.E.: Learning linkage. In: Foundations of Genetic Algorithms IV, pp. 270–285. Morgan Kaufmann, San Mateo (1997)Google Scholar
  3. 3.
    Harik, G.: Linkage learning via probabilistic modeling in the ECGA. IlliGAL Report N. 99010, University of Illinois at Urbana-Champaign, Urbana, IL (1999)Google Scholar
  4. 4.
    Kubalik, J., Rothkrantz, L.J.M., Lazansky, J.: Genetic Algorithm with Limited Convergence. In: Grana, M., Duro, R., d’Anjou, A., Wang, P.P. (eds.) Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations, pp. 216–235. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Kwon, Y.K., Hong, S.D., Moon, B.R.: A Genetic Hybrid For Critical Heat Flux Function Approximation. In: Langdon, W.B., et al. (eds.) Proceedings of GECCO 2002, pp. 1119–1125. Morgan Kaufmann Publishers, San Francisco (2002)Google Scholar
  6. 6.
    Munetomo, M., Goldberg, D.E.: Linkage identification by non-monotonicity detection for overlapping functions. Evolutionary Computation 7(4), 377–398 (1999)CrossRefGoogle Scholar
  7. 7.
    Pelikan, M., Muehlenbein, H.: The Bivariate Marginal Distribution Algorithm. In: Roy, R., Furuhashi, T., Chawdhry, P.K. (eds.) Advances in Soft Computing - Engineering Design and Manufacturing, pp. 521–535. Springer, Heidelberg (1999)Google Scholar
  8. 8.
    Pelikan, M., Goldberg, D.E., Cantu-Paz, E.: Linkage learning, estimation distribution, and Bayesian networks. Evolutionary Computation 8(3), 314–341 (2000)CrossRefGoogle Scholar
  9. 9.
    Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Spector, L., et al. (eds.) Proceedings of the GECCO 2001, pp. 511–518. Morgan Kaufmann, San Francisco (2001)Google Scholar
  10. 10.
    Watson, R.A., Hornby, G.S., Pollack, J.B.: Modeling Building-Block Interdependency. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, Springer, Heidelberg (1998)Google Scholar
  11. 11.
    Whitley, D.: Fundamental Principles of Deception in Genetic Search. In: Rawlins, G. (ed.) Foundations of Genetic Algorithms, pp. 221–241. Morgan Kaufmann, San Francisco (1991)Google Scholar
  12. 12.
    Yu, T.-L., Goldberg, D.E.: Dependency Structure Matrix Analysis: Offline Utility of the Dependency Structure Matrix Genetic Algorithm. In: Deb, K., et al. (eds.) Proceedings of GECCO 2004, pp. 355–366. Springer, Berlin/Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiří Kubalík
    • 1
  • Petr Pošík
    • 1
  • Jan Herold
    • 1
  1. 1.Department of CyberneticsCTU PraguePrague 6Czech Republic

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