Abstract | ||
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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 Deterministic Context-Free L-systems (PMIT-D0L) is implemented based on these techniques. PMIT-D0L is successfully evaluated on 28 known L-systems created by experts with alphabets up to 31 symbols, and PMIT-D0L can successfully infer even the largest of these L-systems in less than a few seconds. It is also evaluated and can correctly infer any system in a larger test set of algorithmically created L-systems with much larger alphabets. |
Year | DOI | Venue |
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2019 | 10.1016/j.swevo.2021.100893 | SWARM AND EVOLUTIONARY COMPUTATION |
Keywords | DocType | Volume |
Lindenmayer systems, Plant modelling, Inductive inference, Genetic Algorithm | Journal | 64 |
ISSN | Citations | PageRank |
2210-6502 | 0 | 0.34 |
References | Authors | |
0 | 2 |
Name | Order | Citations | PageRank |
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Jason Bernard | 1 | 2 | 1.45 |
Ian McQuillan | 2 | 97 | 24.72 |