A GAuGE Approach to Learning DFA from Noisy Samples

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Title: A GAuGE Approach to Learning DFA from Noisy Samples
Authors: Nicolau, Miguel
Ryan, Conor
Ryan, Eoin
Permanent link: http://hdl.handle.net/10197/8339
Date: 30-Jun-2004
Abstract: This paper describes the adaptation of the GAuGE system to classify binary sequences generated by random DFA. Experiments were conducted, which, although not highly successful, illustrate the potential of applying GAuGE like systems to this problem domain.
Type of material: Conference Publication
Keywords: Genetic algorithms;Grammatical evolution
Language: en
Status of Item: Peer reviewed
Conference Details: Genetic and Evolutionary Computation - GECCO 2004: Genetic and Evolutionary Computation Conference, Seattle, Washington, USA, 26-30 June 2004
Appears in Collections:Business Research Collection

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