A Fate of Two Evolutionary Walkers after the Departure from the Origin

  • Akira Imada
Part of the Advances in Soft Computing book series (AINSC, volume 19)


Using Sammon mappings, a method to visualize multidimensional space, we observe a strange difference between two evolutionary searches: Evolutionary Programming and Breeder Genetic Algorithm, when they search for weight solutions of fully connected neural network model of associative memory.


Neural Network Model Synaptic Weight Pattern Space Evolutionary Search Uniform Crossover 
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. Collins, T. D. (1997) Using Software Visualization Technology to Help Evolutionary Algorithm Users Validate their Solutions. Proceedings of the 7th International Conference on Genetic Algorithms, pp.307–314.Google Scholar
  2. Gardner, E. (1988) The Phase Space of Interactions in Neural Network Models. Journal of Physics, 21A, pp. 257–270.Google Scholar
  3. Hopfield, J. J. (1982) Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proc. of the National Academy of Sciences, USA 79, pp. 2554–2558.Google Scholar
  4. Hebb, D. O. (1949) The Organization of Behavior. Wiley.Google Scholar
  5. Komlós, J., and R. Paturi (1988) Convergence Results in an Associative Memory Model. Neural Networks 1, pp. 239–250.Google Scholar
  6. Mühlenbein, H. and D. Schlierkamp-Voosen (1995) Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization. Evolutionary Computation Vol. 1 (1996), pp. 25–49.CrossRefGoogle Scholar
  7. Sammon, J. W. (1969) A Nonlinear Mapping for Data Structure Analysis. IEEE Transactions on Computers. C-18(5), pp.401–408.Google Scholar
  8. Shine, W. B. and C. F. Eick (1997) Visualizing the Evolution of Genetic Algorithm Search Processes. Proceedings of the IEEE International Conference on Evolutionary Computation, pp.367–372.Google Scholar
  9. Syswerda, G. (1989) Uniform Crossover in Genetic Algorithms. Proceedings of the 3rd International Conference on Genetic Algorithms, pp.2–9.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Akira Imada
    • 1
  1. 1.Brest State Technical UniversityBrestRepublic of Belarus

Personalised recommendations