New Techniques for Inferring L-Systems Using Genetic Algorithm
  Lindenmayer systems (L-systems) are a formal grammar system that iteratively
rewrites all symbols of a string, in parallel. When visualized with a graphical
interpretation, the images have self-similar shapes that appear frequently in
nature, and they have been particularly successful as a concise, reusable
technique for simulating plants. The L-system inference problem is to find an
L-system to simulate a given plant. This is currently done mainly by experts,
but this process is limited by the availability of experts, the complexity that
may be solved by humans, and time. This paper introduces the Plant Model
Inference Tool (PMIT) that infers deterministic context-free L-systems from an
initial sequence of strings generated by the system using a genetic algorithm.
PMIT is able to infer more complex systems than existing approaches. Indeed,
while existing approaches are limited to L-systems with a total sum of 20
combined symbols in the productions, PMIT can infer almost all L-systems tested
where the total sum is 140 symbols. This was validated using a test bed of 28
previously developed L-system models, in addition to models created
artificially by bootstrapping larger models.
