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A new crossover operator in genetic programming for object classification.

Mengjie Zhang1, Xiaoying Gao, Weijun Lou

  • 1School of Mathematics, Statistics, and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand. mengjie@mcs.vuw.ac.nz

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 12, 2007
PubMed
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This study introduces a novel crossover operator for genetic programming (GP) in object recognition. The new method enhances classification accuracy by preserving essential program components, outperforming standard GP techniques.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Standard genetic programming (GP) crossover operators often fail to produce effective child programs for object recognition.
  • Random selection of crossover points in traditional GP limits its power in complex tasks like object classification.

Purpose of the Study:

  • To introduce a new crossover operator for genetic programming specifically designed to improve object recognition and classification.
  • To enhance the effectiveness of GP in creating accurate object recognition models by preserving crucial program structures.

Main Methods:

  • Developed a novel GP crossover operator incorporating local hill-climbing search to identify and preserve 'good building blocks'.
  • Introduced a 'looseness' weight to quantify the quality of building blocks within programs.

Related Experiment Videos

  • Utilized looseness values as heuristics to guide the selection of crossover points, preventing the disruption of beneficial program components.

    • Main Results:

    • The proposed approach demonstrated superior performance in object classification accuracy compared to the standard crossover operator and the headless chicken crossover (HCC) method.
    • The new method significantly improved system efficiency when compared to the HCC method, despite a slightly longer processing time than the standard crossover.

    Conclusions:

    • The novel GP crossover operator effectively addresses limitations of standard methods in object recognition tasks.
    • This approach offers a promising strategy for enhancing both accuracy and efficiency in genetic programming-based object classification systems.