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• W3099654263 abstract "A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems." @default.
• W3099654263 created "2020-11-23" @default.
• W3099654263 creator A5033221821 @default.
• W3099654263 creator A5076087744 @default.
• W3099654263 date "2013-04-17" @default.
• W3099654263 modified "2023-10-16" @default.
• W3099654263 title "Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system" @default.
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• W3099654263 doi "https://doi.org/10.1007/s00500-013-1044-4" @default.
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