Techniques for Inferring Context-Free Lindenmayer Systems With Genetic Algorithm
This addresses the labor-intensive task for experts in fields like biological modeling by automating L-system inference, though it appears incremental as it builds on existing genetic algorithm and L-system techniques.
The paper tackles the problem of manually creating Lindenmayer systems (L-systems) by developing a genetic algorithm-based tool, PMIT-D0L, to infer context-free L-systems from sequences of strings; it successfully inferred 28 known L-systems with alphabets up to 31 symbols in less than a few seconds.
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.