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Investigating scaling of an abstracted LCS utilising ternary and S-Expression alphabets

Ioannides, C. and Browne, W. (2007) Investigating scaling of an abstracted LCS utilising ternary and S-Expression alphabets. In: 11th International Workshop, IWLCS 2007, London, UK, https://doi.org/10.1007/978-3-540-88138-4 .

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To link to this item DOI: 10.1007/978-3-540-88138-4

Abstract/Summary

Utilising the expressive power of S-Expressions in Learning Classifier Systems often prohibitively increases the search space due to increased flexibility of the endcoding. This work shows that selection of appropriate S-Expression functions through domain knowledge improves scaling in problems, as expected. It is also known that simple alphabets perform well on relatively small sized problems in a domain, e.g. ternary alphabet in the 6, 11 and 20 bit MUX domain. Once fit ternary rules have been formed it was investigated whether higher order learning was possible and whether this staged learning facilitated selection of appropriate functions in complex alphabets, e.g. selection of S-Expression functions. This novel methodology is shown to provide compact results (135-MUX) and exhibits potential for scaling well (1034-MUX), but is only a small step towards introducing abstraction to LCS.

Item Type:Conference or Workshop Item (Paper)
Divisions:Faculty of Science
ID Code:14728
Publisher:Springer

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