Evolutionary Optimization of Heterogeneous Problems

  • Lluís A. Belanche Muñoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


A large number of practical optimization problems involve elements of quite diverse nature described as mixtures of qualitative and quantitative information and whose description is possibly incomplete. In this work we present an extension of the breeder genetic algorithm that represents and manipulates this heterogeneous information in a natural way. The algorithm is illustrated in a set of optimization tasks involving the training of different kinds of neural networks. An extensive experimental study is presented in order to show the potential of the algorithm.


Neural Network Heart Disease Genetic Algorithm Operating System Problem Complexity 
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.
    Palmer, C. C., Kershenbaum, A. Representing trees in genetic algorithms. In Bäck, Th., Fogel D. B., Michalewicz, Z. (Eds.) Handbook of Evolutionary Computation. IOP Publishing & Oxford Univ. Press, 1997.Google Scholar
  2. 2.
    Mühlenbein, H., Schlierkamp-Voosen, D. Predictive Models for the Breeder Genetic Algorithm. Evolutionary Computation, 1(1): 25–49, 1993.CrossRefGoogle Scholar
  3. 3.
    Bäck, Th. Evolutionary Algorithms in Theory and Practice. Oxford Press, 1996.Google Scholar
  4. 4.
    Voigt, H. M., Mühlenbein, H., Cvetkovic, D. Fuzzy recombination for the continuous Breeder Genetic Algorithm. In Procs. of ICGA’95.Google Scholar
  5. 5.
    Balakrishnan, K., Honavar, V. Evolutionary design of neural architectures—a preliminary taxonomy and guide to literature. Technical report CS-TR-95-01. Dept. of Computer Science. Iowa State Univ., 1995.Google Scholar
  6. 6.
    Yao, X. Evolving Artificial Neural Networks. Procs. of the IEEE, 87(9), 1999.Google Scholar
  7. 7.
    De Falco, I., Iazzetta, A, Natale, P., Tarantino, E. Evolutionary Neural Networks for Nonlinear Dynamics Modeling. In Procs. of PPSN V, Amsterdam, 1998.Google Scholar
  8. 8.
    Zhang, B. T., Mühlenbein, H. Evolving Optimal Neural Networks Using Genetic Algorithms with Occam’s Razor. Complex Systems, 7(3): 199–220, 1993.Google Scholar
  9. 9.
    Gower, J. C. A General Coefficient of Similarity and some of its Properties. Biometrics, 27: 857–871, 1971.CrossRefGoogle Scholar
  10. 10.
    Valdes J. J., Belanche, LI., Alquezar, R. Fuzzy Heterogeneous Neurons for Imprecise Classification Problems. Intl. Journal of Intelligent Systems, 15(3): 265–276, 2000.zbMATHCrossRefGoogle Scholar
  11. 11.
    Belanche, LI. Heterogeneous neural networks: theory and applications. Ph.D. Thesis. Universitat Politècnica de Catalunya, Barcelona, Spain, 2000.Google Scholar
  12. 12.
    Prechelt, L. Probenl: A set of Neural Network Benchmark Problems and Benchmarking Rules. Facultät für Informatik. Univ. Karlsruhe. Tech. Rep. 21/94, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Lluís A. Belanche Muñoz
    • 1
  1. 1.Dept. de Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations