A genetics-based technique for the automated acquisition of expert system rule bases

Johnson, Chassidy; Feyock, Stefan

doi:10.1109/dmesp.1991.171705

Cited by 2 publications

(2 citation statements)

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“…All use a genotype to phenotype mapping, where the genotype is a linear sequence, and an intermediate state is constructed in the form of a derivation tree of the grammar, which is then further decoded to an expression tree. The work of Johnson and Feyock [39] prefigured these approaches by several years, but was not widely available and so did not influence the subsequent development of the field.…”

Section: Linearised Gggpmentioning

confidence: 99%

“…It is an extension of the GGGP systems with a linear representation described above. Three innovations were incorporated in GE: variable length (although these had been previously considered by Johnson and Feyock [39]), redundancy using the MOD mapping rule, and the wrapping operation. The chromosome in GE typically has variable length rather than being fixed, although fixed-length instances have been adopted [66,53].…”

Section: Linearised Gggpmentioning

confidence: 99%

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Grammar-based Genetic Programming: a survey

McKay

Hoài

Whigham

et al. 2010

Genet Program Evolvable Mach

322

138

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Publication informationGenetic Programming and Evolvable Machines, Abstract Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.

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Section: Linearised Gggpmentioning

confidence: 99%

Section: Linearised Gggpmentioning

confidence: 99%