  Grammars provide a convenient and powerful mechanism to define the space of
possible solutions for a range of problems. However, when used in grammatical
evolution (GE), great care must be taken in the design of a grammar to ensure
that the polymorphic nature of the genotype-to-phenotype mapping does not
impede search. Additionally, recent work has highlighted the importance of the
initialisation method on GE's performance. While recent work has shed light on
the matters of initialisation and grammar design with respect to GE, their
impact on other methods, such as random search and context-free grammar genetic
programming (CFG-GP), is largely unknown. This paper examines GE, random search
and CFG-GP under a range of benchmark problems using several different
initialisation routines and grammar designs. The results suggest that CFG-GP is
less sensitive to initialisation and grammar design than both GE and random
search: we also demonstrate that observed cases of poor performance by CFG-GP
are managed through simple adjustment of tuning parameters. We conclude that
CFG-GP is a strong base from which to conduct grammar-guided evolutionary
search, and that future work should focus on understanding the parameter space
of CFG-GP for better application.
