Grammatical Evolution Strategies for Bioinformatics and Systems Genomics
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- @InCollection{Moore:2018:hbge,
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author = "Jason H. Moore and Moshe Sipper",
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title = "Grammatical Evolution Strategies for Bioinformatics
and Systems Genomics",
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booktitle = "Handbook of Grammatical Evolution",
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publisher = "Springer",
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year = "2018",
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editor = "Conor Ryan and Michael O'Neill and J. J. Collins",
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chapter = "16",
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pages = "395--405",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-3-319-78716-9",
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DOI = "doi:10.1007/978-3-319-78717-6_16",
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abstract = "Evolutionary computing methods are an attractive
option for modelling complex biological and biomedical
systems because they are inherently parallel, they
conduct stochastic search through large solution
spaces, they capitalize on the modularity of solutions,
they have flexible solution representations, they can
use expert knowledge, they can consider multiple
fitness criteria, and they are inspired by how
evolution optimizes fitness through natural selection.
Grammatical evolution (GE) is a promising example of
evolutionary computing because it generates solutions
to a problem using a generative grammar. We review here
several detailed examples of GE from the bioinformatics
and systems genomics literature and end with some ideas
about the challenges and opportunities for integrating
GE into biological and biomedical discovery.",
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notes = "Part of \cite{Ryan:2018:hbge}",
- }
Genetic Programming entries for
Jason H Moore
Moshe Sipper
Citations