Deriving genetic programming fitness properties by static analysis
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gp-bibliography.bib Revision:1.8028
- @InProceedings{johnson:2002:EuroGP,
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title = "Deriving genetic programming fitness properties by
static analysis",
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author = "Colin G. Johnson",
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editor = "James A. Foster and Evelyne Lutton and
Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
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booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
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volume = "2278",
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series = "LNCS",
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pages = "298--307",
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publisher = "Springer-Verlag",
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address = "Kinsale, Ireland",
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publisher_address = "Berlin",
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month = "3-5 " # apr,
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year = "2002",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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ISBN = "3-540-43378-3",
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URL = "http://www.cs.kent.ac.uk/pubs/2002/1351/content.ps",
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URL = "http://www.cs.ukc.ac.uk/pubs/2002/1351",
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DOI = "doi:10.1007/3-540-45984-7_29",
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abstract = "The aim of this paper is to introduce the idea of
using static analysis of computer programs as a way of
measuring fitness in genetic programming. Such
techniques extract information about the programs
without explicitly running them, and in particular they
infer properties which hold across the whole of the
input space of a program. This can be applied to
measure fitness, and has a number of advantages over
measuring fitness by running members of the population
on test cases. The most important advantage is that if
a solution is found then it is possible to formally
trust that solution to be correct across all inputs.
This paper introduces these ideas, discusses various
ways in which they could be applied, discusses the type
of problems for which they are appropriate, and ends by
giving a simple test example and some questions for
future research.",
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notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
- }
Genetic Programming entries for
Colin G Johnson
Citations