A GAuGE Approach to Learning DFA from Noisy Samples
|Title:||A GAuGE Approach to Learning DFA from Noisy Samples||Authors:||Nicolau, Miguel
|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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