Genetic Algorithms Using Grammatical Evolution
|Title:||Genetic Algorithms Using Grammatical Evolution||Authors:||Ryan, Conor
|Permanent link:||http://hdl.handle.net/10197/8189||Date:||5-Apr-2002||Abstract:||This paper describes the GAUGE system, Genetic Algorithms Using Grammatical Evolution. GAUGE is a position independent Genetic Algorithm that uses Grammatical Evolution with an attribute grammar to dictate what position a gene codes for. GAUGE suffers from neither under-specification nor over-specification, is guaranteed to produce syntactically correct individuals, and does not require any repair after the application of genetic operators. GAUGE is applied to the standard onemax problem, with results showing that its genotype to phenotype mapping and position independence nature do not affect its performance as a normal genetic algorithm. A new problem is also presented, a deceptive version of the Mastermind game, and we show that GAUGE possesses the position independence characteristics it claims, and outperforms several genetic algorithms, including the competent genetic algorithm messyGA.||Type of material:||Conference Publication||Publisher:||Springer||Copyright (published version):||2002 Springer||Keywords:||Grammatical evolution; Genetic algorithms; Onemax problem; Mastermind||DOI:||10.1007/3-540-45984-7_27||Language:||en||Status of Item:||Peer reviewed||Is part of:||Foster, J. A., Lutton, E., Miller, J., Ryan, C. and Tettamanzi, A. (eds.). Genetic Programming: 5th European Conference (EuroGP) (Volume 2278)||Conference Details:||Genetic Programming: 5th European Conference (EuroGP), Kinsale, Co. Cork, Ireland, 3-5 April 2002||ISBN:||9783540433781||metadata.dc.date.available:||2016-12-02T17:40:58Z|
|Appears in Collections:||Business Research Collection|
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