Layered Learning in Boolean GP Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{eurogp07:jackson,
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author = "David Jackson and Adrian P. Gibbons",
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title = "Layered Learning in {Boolean} GP Problems",
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editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
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booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "4445",
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year = "2007",
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address = "Valencia, Spain",
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month = "11-13 " # apr,
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pages = "148--159",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-71602-5",
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isbn13 = "978-3-540-71602-0",
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DOI = "doi:10.1007/978-3-540-71605-1_14",
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abstract = "Layered learning is a decomposition and reuse
technique that has proved to be effective in the
evolutionary solution of difficult problems. Although
previous work has integrated it with genetic
programming (GP), much of the application of that
research has been in relation to multi-agent systems.
In extending this work, we have applied it to more
conventional GP problems, specifically those involving
Boolean logic. We have identified two approaches which,
unlike previous methods, do not require prior
understanding of a problem's functional decomposition
into sub-goals. Experimentation indicates that although
one of the two approaches offers little advantage, the
other leads to solution-finding performance
significantly surpassing that of both conventional GP
systems and those which incorporate automatically
defined functions.",
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notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
- }
Genetic Programming entries for
David Jackson
Adrian P Gibbons
Citations