Learnable Embeddings of Program Spaces
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{krawiec:2011:EuroGP,
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author = "Krzysztof Krawiec",
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title = "Learnable Embeddings of Program Spaces",
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booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
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year = "2011",
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month = "27-29 " # apr,
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editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
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series = "LNCS",
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volume = "6621",
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publisher = "Springer Verlag",
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address = "Turin, Italy",
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pages = "166--177",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-20406-7",
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DOI = "doi:10.1007/978-3-642-20407-4_15",
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abstract = "We consider a class of adaptive, globally-operating,
semantic-based embeddings of programs into discrete
multidimensional spaces termed prespaces. In the
proposed formulation, the original space of programs
and its prespace are bound with a learnable mapping,
where the process of learning is aimed at improving the
overall locality of the new representation with respect
to program semantics. To learn the mapping, which is
formally a permutation of program locations in the
prespace, we propose two algorithms: simple greedy
heuristics and an evolutionary algorithm. To guide the
learning process, we use a new definition of semantic
locality. In an experimental illustration concerning
four symbolic regression domains, we demonstrate that
an evolutionary algorithm is able to improve the
embedding designed by means of greedy search, and that
the learnt prespaces usually offer better search
performance than the original program space.",
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notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
- }
Genetic Programming entries for
Krzysztof Krawiec
Citations