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- W48799606 abstract "L-system is a bio-inspired computational model to capture growth process of plants. This paper proposes a new noise-tolerant grammatical induction LGIC2 for deterministic context-free L-systems. LGIC2 induces L-system grammars from a transmuted string mY, employing an emergent approach in order to enforce its noise tolerance. In the method, frequently appearing substrings are extracted from mY to form grammar candidates. A grammar candidate is used to generate a string Z; however, the number of grammar candidates gets huge, meaning enormous computational cost. Thus, how to prune grammar candidates is vital here. We introduce a couple of techniques such as pruning by frequency, pruning by goodness of fit, and pruning by contractive embedding. Finally, several candidates having the strongest similarities between mY and Z are selected as the final solutions. Our experiments using insertion-type transmutation showed that LGIC2 worked very nicely, much better than an enumerative method LGIC1." @default.
- W48799606 created "2016-06-24" @default.
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- W48799606 date "2014-01-01" @default.
- W48799606 modified "2023-09-23" @default.
- W48799606 title "Emergent Induction of Deterministic Context-Free L-system Grammar" @default.
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- W48799606 doi "https://doi.org/10.1007/978-3-319-01781-5_7" @default.
- W48799606 hasPublicationYear "2014" @default.
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