abstract = "A long-standing problem in Evolutionary Computation
consists in how to choose an appropriate representation
for the solutions. In this work we investigate the
feasibility of synthesizing a representation
automatically, for the large class of problems whose
solution spaces can be defined by a context-free
grammar. We propose a framework based on a form of
meta-evolution in which individuals are candidate
representations expressed with an ad hoc language that
we have developed to this purpose. Individuals compete
and evolve according to an evolutionary search aimed at
optimizing such representation properties as
redundancy, locality, uniformity of redundancy. We
assessed experimentally three variants of our framework
on established benchmark problems and compared the
resulting representations to human-designed
representations commonly used (e.g., classical
Grammatical Evolution). The results are promising in
the sense that the evolved representations indeed
exhibit better properties than the human-designed ones.
Furthermore, while those improved properties do not
result in a systematic improvement of search
effectiveness, some of the evolved representations do
improve search effectiveness over the human-designed
baseline.",