- author = "Alberto Bartoli and Mauro Castelli and Eric Medvet",
- title = "Weighted Hierarchical Grammatical Evolution",
- journal = "IEEE Transactions on Cybernetics",
- year = "2020",
- volume = "50",
- number = "2",
- pages = "476--488",
- month = feb,
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution, genotype-phenotype, mapping, representation",
- ISSN = "2168-2267",
-
URL = "
https://sites.google.com/site/machinelearningts/publications/international-journal-publications/weightedhierarchicalgrammaticalevolution/2018-TCyb-WHGE.pdf",
-
DOI = "
doi:10.1109/TCYB.2018.2876563",
- size = "13 pages",
-
abstract = "Grammatical Evolution (GE) is one of the most
widespread techniques in evolutionary computation.
Genotypes in GE are bit strings while phenotypes are
strings of a language defined by a user-provided
context-free grammar (CFG). In this work, we propose a
novel procedure for mapping genotypes to phenotypes
that we call Weighted Hierarchical GE (WHGE). WHGE
imposes a form of hierarchy on the genotype and encodes
grammar symbols with a varying number of bits based on
the relative expressive power of those symbols. WHGE
does not impose any constraint on the overall GE
framework, in particular, WHGE may handle recursive
grammars, uses the classical genetic operators, and
does not need to define any bound in advance on the
size of phenotypes.
We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results to the standard GE framework as well as to two of the most significant enhancements proposed in the literature: Position-independent GE and Structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.",
- notes = "also known as \cite{8525307}",
