Geometric semantic genetic programming with normalized and standardized random programs
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Bakurov:2024:GPEM,
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author = "Illya Bakurov and Jose Manuel {Munoz Contreras} and
Mauro Castelli and Nuno Rodrigues and Sara Silva and
Leonardo Trujillo and Leonardo Vanneschi",
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title = "Geometric semantic genetic programming with normalized
and standardized random programs",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2024",
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volume = "25",
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pages = "Article no 6",
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note = "Online first",
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keywords = "genetic algorithms, genetic programming, Geometric
semantic mutation, Internal covariate shift, Sigmoid
distribution bias, Model simplification",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/dysci",
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DOI = "doi:10.1007/s10710-024-09479-1",
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size = "29 pages",
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abstract = "Geometric semantic genetic programming (GSGP)
represents one of the most promising developments in
the area of evolutionary computation (EC) in the last
decade. The results achieved by incorporating semantic
awareness in the evolutionary process demonstrate the
impact that geometric semantic operators have brought
to the field of EC. An improvement to the geometric
semantic mutation (GSM) operator is proposed, inspired
by the results achieved by batch normalization in deep
learning. While, in one of its most used versions, GSM
relies on the use of the sigmoid function to constrain
the semantics of two random programs responsible for
perturbing the parent semantics, here a different
approach is followed, which allows reducing the size of
the resulting programs and overcoming the issues
associated with the use of the sigmoid function, as
commonly done in deep learning. The idea is to consider
a single random program and use it to perturb the
parent’s semantics only after standardization or
normalization. The experimental results demonstrate the
suitability of the proposed approach: despite its
simplicity, the presented GSM variants outperform
standard GSGP on the studied benchmarks, with a
difference in terms of performance that is
statistically significant. Furthermore, the individuals
generated by the new GSM variants are easier to
simplify, allowing us to create accurate but
significantly smaller solutions.",
- }
Genetic Programming entries for
Illya Bakurov
Jose Manuel Munoz Contreras
Mauro Castelli
Nuno Miguel Rodrigues Domingos
Sara Silva
Leonardo Trujillo
Leonardo Vanneschi
Citations