Modification Point Depth and Genome Growth in Genetic
Programming
Evolutionary Computation, 11 (1):
67--106 (Spring 2003)DOI: doi:10.1162/106365603321829014
Abstract
The evolutionary computation community has shown
increasing interest in arbitrary-length
representations, particularly in the field of genetic
programming. A serious stumbling block to the
scalability of such representations has been bloat:
uncontrolled genome growth during an evolutionary run.
Bloat appears across the evolutionary computation
spectrum, but genetic programming has given it by far
the most attention. Most genetic programming models
explain this phenomenon as a result of the growth of
introns, areas in an individual which serve no
functional purpose. This paper presents evidence which
directly contradicts intron theories as applied to
tree-based genetic programming. The paper then uses
data drawn from this evidence to propose a new model of
genome growth. In this model, bloat in genetic
programming is a function of the mean depth of the
modification (crossover or mutation) point. Points far
from the root are correspondingly less likely to hurt
the child's survivability in the next generation. The
modication point is in turn strongly correlated to
average parent tree size and to removed subtree size,
both of which are directly linked to the size of the
resulting child.
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