# Techniques for Inferring Context-Free Lindenmayer Systems With Genetic Algorithm

## TL;DR

This paper presents a genetic algorithm-based method and a new tool, PMIT-D0L, for automatically inferring context-free L-systems from string sequences, significantly reducing inference time for complex systems.

## Contribution

The paper introduces PMIT-D0L, a novel tool leveraging genetic algorithms for efficient inference of context-free L-systems, improving speed and accuracy over previous methods.

## Key findings

- Successfully inferred 28 known L-systems with up to 31 symbols.
- PMIT-D0L infers large L-systems in less than a few seconds.
- Evaluated different encoding schemes and mathematical properties for inference.

## Abstract

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.