Geometric Semantic Grammatical Evolution
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- @InCollection{Moraglio:2018:hbge,
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author = "Alberto Moraglio and James McDermott and
Michael O'Neill",
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title = "Geometric Semantic Grammatical Evolution",
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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 = "7",
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pages = "163--188",
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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_7",
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abstract = "Geometric Semantic Genetic Programming (GSGP) is a
novel form of Genetic Programming (GP), based on a
geometric theory of evolutionary algorithms, which
directly searches the semantic space of programs. In
this chapter, we extend this framework to Grammatical
Evolution (GE) and refer to the new method as Geometric
Semantic Grammatical Evolution (GSGE). We formally
derive new mutation and crossover operators for GE
which are guaranteed to see a simple unimodal fitness
landscape. This surprising result shows that the GE
genotype-phenotype mapping does not necessarily imply
low genotype-fitness locality. To complement the
theory, we present extensive experimental results on
three standard domains (Boolean, Arithmetic and
Classifier).",
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notes = "Part of \cite{Ryan:2018:hbge}",
- }
Genetic Programming entries for
Alberto Moraglio
James McDermott
Michael O'Neill
Citations