Abstract
Although genetic programming has often successfully
been applied to non-parametric modeling, it is
frequently impaired by the huge size of the search
space explored. Domain knowledge is a powerful way to
trim out the size of the space, by restricting the
search to a priori relevant models. A most natural
domain knowledge in scientific modeling is known as
dimensional analysis, stipulating that the models must
be consistent with regards to the variable measurement
units.In this paper, it is shown that dimensional
analysis can automatically be expressed as a context
free grammar. Dimensionally-aware GP is thus achieved
by employing the dimensional grammar within the
grammar-guided GP framework first investigated by Gruau
On using syntactic constraints with genetic
programming, in: P. Angeline, K.E. Kinnear Jr. (Eds.),
Advances in Genetic Programming II, MIT Press,
Cambridge, MA, 1996, pp. 377-394.. However,
grammar-guided genetic programming encounters severe
difficulties when it involves a complex grammar, which
might explain why this approach has not been widely
used so far. The drawback is blamed on the
initialization step, which hardly constructs a
sufficiently diversified initial population, thus
hindering the success of evolution. This limitation is
addressed by a new CFG compliant initialization
procedure.The approach is validated on two problems
related to the identification of mechanical properties
of materials.
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