Grammatical Evolution Strategies for Bioinformatics and Systems Genomics

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{Moore:2018:hbge,

• author = "Jason H. Moore and Moshe Sipper",
• title = "Grammatical Evolution Strategies for Bioinformatics and Systems Genomics",
• booktitle = "Handbook of Grammatical Evolution",
• publisher = "Springer",
• year = "2018",
• editor = "Conor Ryan and Michael O'Neill and J. J. Collins",
• chapter = "16",
• pages = "395--405",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution",
• isbn13 = "978-3-319-78716-9",
• DOI = "doi:10.1007/978-3-319-78717-6_16",
• 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.",
• notes = "Part of \cite{Ryan:2018:hbge}",

}

Genetic Programming entries for Jason H Moore Moshe Sipper

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