Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings
Created by W.Langdon from
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- @Article{Wilson:2007:GPEM,
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author = "Garnett Wilson and Malcolm Heywood",
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title = "Introducing probabilistic adaptive mapping
developmental genetic programming with redundant
mappings",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2007",
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volume = "8",
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number = "2",
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pages = "187--220",
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month = jun,
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note = "Special issue on developmental systems",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-007-9027-9",
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size = "34 pages",
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abstract = "Developmental Genetic Programming (DGP) algorithms
have explicitly required the search space for a problem
to be divided into genotypes and corresponding
phenotypes. The two search spaces are often connected
with a genotype-phenotype mapping (GPM) intended to
model the biological genetic code, where current
implementations of this concept involve evolution of
the mappings along with evolution of the genotype
solutions. This work presents the Probabilistic
Adaptive Mapping DGP (PAM DGP), a new developmental
implementation that involves research contributions in
the areas of GPMs and coevolution. The algorithm
component of PAM DGP is demonstrated to overcome
coevolutionary performance problems that are identified
and empirically benchmarked against the latest
competing algorithm that adapts similar GPMs. An
adaptive redundant mapping encoding is then
incorporated into PAM DGP for further performance
enhancement. PAM DGP with two mapping types are
compared to the competing Adaptive Mapping algorithm
and Traditional GP in two medical classification
domains, where PAM DGP with redundant encodings is
found to provide superior fitness performance over the
other algorithms through its ability to explicitly
decrease the size of the function set during
evolution.",
- }
Genetic Programming entries for
Garnett Carl Wilson
Malcolm Heywood
Citations