Abstract
Previous work on introns and code growth in genetic
programming is expanded on and tested experimentally.
Explicitly defined introns are introduced to tree-based
representations as an aid to measuring and evaluating
intron behavior. Although it is shown that introns do
create code growth, they are not its only cause.
Removing introns merely decreases the growth rate; it
does not eliminate it. By systematically negating
various forms of intron behavior, a deeper
understanding of the causes of code growth is obtained,
leading to the development of a system that keeps
unnecessary bloat to a minimum. Alternative selection
schemes and recombination operators are examined and
improvements demonstrated over the standard selection
methods in terms of both performance and parsimony.
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