GP for object classification: brood size in brood recombination crossover

  • Authors:

  • Mengjie Zhang;Xiaoying Gao;Weijun Lou

  • Affiliations:

  • School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand

  • Venue:
  • AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
  • Year:
  • 2006

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Abstract

The brood size plays an important role in the brood recombination crossover method in genetic programming. However, there has not been any thorough investigation on the brood size and the methods for setting this size have not been effectively examined. This paper investigates a number of new developments of brood size in the brood recombination crossover method in GP. We first investigate the effect of different fixed brood sizes, then construct three dynamic models for setting the brood size. These developments are examined and compared with the standard crossover operator on three object classification problems of increasing difficulty. The results suggest that the brood recombination methods with all the new developments outperforms the standard crossover operator for all the problems. As the brood size increases, the system effective performance can be improved. When it exceeds a certain point, however, the effective performance will not be improved and the system will become less efficient. Investigation of three dynamic models for the brood size reveals that a good variable brood size which is dynamically set with the number of generations can further improve the system performance over the fixed brood sizes.