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Code Growth, Explicitly Defined Introns, and Alternative Selection Schemes

Smith1, Harries

  • 1Department of Computer Science, The City University, Northampton Square, London, EC1V OHB, United Kingdom. peters@soi.city.ac.uk

Evolutionary Computation
|February 9, 1999
PubMed
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Introns in genetic programming contribute to code growth but are not the sole cause. New methods minimize bloat, improving performance and parsimony using alternative selection and recombination.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Genetic programming (GP) often suffers from code bloat, leading to inefficient program representations.
  • Introns, non-coding sequences in genetic material, have been hypothesized to influence code growth in GP.

Purpose of the Study:

  • To experimentally investigate the role of introns in code growth within tree-based genetic programming.
  • To develop strategies for minimizing code bloat by understanding and controlling intron behavior.

Main Methods:

  • Explicitly defined introns were introduced into tree-based representations in genetic programming.
  • Systematic negation of intron behaviors was performed to isolate their effects on code growth.
  • Alternative selection schemes and recombination operators were evaluated.

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Main Results:

  • Introns were confirmed to contribute to code growth, but other factors are also involved.
  • Removing introns reduced, but did not eliminate, code growth.
  • A system was developed to minimize unnecessary code bloat.
  • Alternative selection and recombination methods showed improvements in performance and parsimony.

Conclusions:

  • Code growth in genetic programming is a multifactorial phenomenon.
  • Controlling intron behavior is a viable strategy for bloat reduction.
  • Optimized selection and recombination operators enhance GP efficiency and solution quality.