Multi-level diversity promotion strategies for Grammar-guided Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{BARTOLI:2019:ASC,
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author = "Alberto Bartoli and Andrea {De Lorenzo} and
Eric Medvet and Giovanni Squillero",
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title = "Multi-level diversity promotion strategies for
Grammar-guided Genetic Programming",
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journal = "Applied Soft Computing",
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volume = "83",
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pages = "105599",
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year = "2019",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2019.105599",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494619303795",
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keywords = "genetic algorithms, genetic programming,
Representation, Grammatical evolution, CFGGP, SGE,
WHGE",
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abstract = "Grammar-guided Genetic Programming (G3P) is a family
of Evolutionary Algorithms that can evolve programs in
any language described by a context-free grammar. The
most widespread members of this family are based on an
indirect representation: a sequence of bits or integers
(the genotype) is transformed into a string of the
language (the phenotype) by means of a mapping
function, and eventually into a fitness value.
Unfortunately, the flexibility brought by this mapping
is also likely to introduce non-locality phenomena,
reduce diversity, and hamper the effectiveness of the
algorithm. In this paper, we experimentally
characterize how population diversity, measured at
different levels, varies for four popular G3P
approaches. We then propose two strategies for
promoting diversity which are general, independent both
from the specific problem being tackled and from the
other components of the Evolutionary Algorithm, such as
genotype-phenotype mapping, selection criteria, and
genetic operators. We experimentally demonstrate their
efficacy in a wide range of conditions and from
different points of view. The results also confirm the
preponderant importance of the phenotype-level analyzes
in diversity promotion",
- }
Genetic Programming entries for
Alberto Bartoli
Andrea De Lorenzo
Eric Medvet
Giovanni Squillero
Citations