abstract = "The problem of evolving an artificial ant to follow
the Santa Fe trail is used to demonstrate the well
known genetic programming feature of growth in solution
length. Known variously as ``bloat'', ``redundancy'',
``introns'', ``fluff'', ``Structural Complexity'' with
antonyms ``parsimony'', ``Minimum Description Length''
(MDL) and ``Occam's razor''. Comparison with runs with
and without fitness selection pressure shows the
tendency for solutions to grow in size is caused by
fitness based selection. We argue that such growth is
inherent in using a fixed evaluation function with a
discrete but variable length representation. Since with
simple static evaluation search converges to mainly
finding trial solutions with the same fitness as
existing trial solutions. In general variable length
allows many more long representations of a given
solution than short ones of the same solution. Thus
with an unbiased random search we expect longer
representations to occur more often and so
representation length tends to increase. I.e. fitness
based selection leads to bloat.",
notes = "WSC2 Second On-line World Conference on Soft Computing
in Engineering Design and Manufacturing extends
\cite{Langdon:1997:bloatTR}