abstract = "Compression algorithms generate a predictive model of
data, using the model to reduce the number of bits
required to transmit the data (in effect, transmitting
only the differences from the model). As a consequence,
the degree of compression achieved provides an estimate
of the level of regularity in the data. Previous work
has investigated the use of these estimates to
understand the replication of building blocks within
Genetic Programming (GP) individuals, and hence to
understand how different GP algorithms promote the
evolution of repeated common structure within
individuals. Here, we extend this work to the
population level, and use it to understand the extent
of similarity between sub-structures within individuals
in GP populations.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.