@inproceedings{a155c1b06f5a41b8a403380cdbcf433d,
title = "New techniques for inferring l-systems using genetic algorithm",
abstract = "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 been particularly successful as a concise method for simulating plants. Creating L-systems to simulate a given plant manually by experts is limited by the availability of experts 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 can infer D0L-Systems where the sum of production successors is 20, PMIT can infer those where the sum is 140. This was validated using a testbed of 28 known D0L-system models, in addition to models created artificially by bootstrapping larger models.",
keywords = "Genetic algorithm, Inductive inference, L-systems, Plant modeling",
author = "Jason Bernard and Ian McQuillan",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 8th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2018 ; Conference date: 16-05-2018 Through 18-05-2018",
year = "2018",
doi = "10.1007/978-3-319-91641-5\_2",
language = "English",
isbn = "9783319916408",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "13--25",
editor = "Nouredine Melab and Peter Korosec and El-Ghazali Talbi",
booktitle = "Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings",
}