  Lindenmayer systems (L-systems) are a formal grammar system, where the most
notable feature is a set of rewriting rules that are used to replace every
symbol in a string in parallel; by repeating this process, a sequence of
strings is produced. Some symbols in the strings may be interpreted as
instructions for simulation software. Thus, the sequence can be used to model
the steps of a process. Currently, creating an L-system for a specific process
is done by hand by experts through much effort. The inductive inference problem
attempts to infer an L-system from such a sequence of strings generated by an
unknown system; this can be thought of as an intermediate step to inferring
from a sequence of images. This paper evaluates and analyzes different genetic
algorithm encoding schemes and mathematical properties for the L-system
inductive inference problem. A new tool, the Plant Model Inference Tool for
Context-Free L-systems (PMIT-D0L) is implemented based on these techniques.
PMIT-D0L has been successfully evaluated on 28 known L-systems, with alphabets
up to 31 symbols and a total sum of 281 symbols across the rewriting rules.
PMIT-D0L can infer even the largest of these L-systems in less than a few
seconds.
