A Fine-Grained View of Phenotypes and Locality in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{McDermott:2011:GPTP,
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author = "James McDermott and Edgar Galvan-Lopez and
Michael O'Neill",
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title = "A Fine-Grained View of Phenotypes and Locality in
Genetic Programming",
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booktitle = "Genetic Programming Theory and Practice IX",
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year = "2011",
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editor = "Rick Riolo and Ekaterina Vladislavleva and
Jason H. Moore",
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series = "Genetic and Evolutionary Computation",
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address = "Ann Arbor, USA",
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month = "12-14 " # may,
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publisher = "Springer",
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chapter = "4",
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pages = "57--76",
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keywords = "genetic algorithms, genetic programming, Evolutionary
computation, fitness landscape, problem difficulty,
phenotype, locality, artificial ant, Boolean problems",
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isbn13 = "978-1-4614-1769-9",
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DOI = "doi:10.1007/978-1-4614-1770-5_4",
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abstract = "The locality of the mapping from genotype to phenotype
is an important issue in the study of landscapes and
problem difficulty in evolutionary computation. In
tree-structured Genetic Programming (GP), the locality
approach is not generally applied because no explicit
genotype-phenotype mapping exists, in contrast to some
other GP encodings. we define GP phenotypes in terms of
semantics or behaviour. For a given problem, a model of
one or more phenotypes and mappings between them may be
appropriate e.g. g -> p_0, where g is the genotype, p_i
are distinct types of phenotypes and f is fitness.
Thus, the behaviour of each component mapping can be
studied separately. The locality of the
genotype-phenotype mapping can also be decomposed into
the effects of the encoding and those of the operator's
genotypic step-size. Two standard benchmark problem
classes, Boolean and artificial ant, are studied in a
principled way using this fine-grained view of
locality. The method of studying locality with
phenotypes seems useful in the case of the artificial
ant, but Boolean problems provide a counter-example.",
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notes = "part of \cite{Riolo:2011:GPTP}",
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affiliation = "Evolutionary Design and Optimization, CSAIL, MIT,
Cambridge, USA",
- }
Genetic Programming entries for
James McDermott
Edgar Galvan Lopez
Michael O'Neill
Citations