New Techniques for Inferring L-Systems Using Genetic Algorithm
This addresses the automation of plant simulation modeling for fields like computer graphics or biology, reducing reliance on expert input, but it is incremental as it builds on existing genetic algorithm methods for a specific domain.
The paper tackles the L-system inference problem by introducing the Plant Model Inference Tool (PMIT), which uses a genetic algorithm to infer deterministic context-free L-systems from initial sequences, achieving the ability to infer systems with up to 140 combined symbols compared to existing limits of 20 symbols.
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.