Semantic Sub-tree Crossover Operator for Postfix Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{conf/bic-ta/DabhiC12,
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author = "Vipul K. Dabhi and Sanjay Chaudhary",
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title = "Semantic Sub-tree Crossover Operator for Postfix
Genetic Programming",
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booktitle = "Proceedings of Seventh International Conference on
Bio-Inspired Computing: Theories and Applications
(BIC-TA 2012)",
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year = "2012",
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editor = "Jagdish Chand Bansal and Pramod Kumar Singh and
Kusum Deep and Millie Pant and Atulya Nagar",
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volume = "201",
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series = "Advances in Intelligent Systems and Computing",
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pages = "391--402",
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publisher_address = "India",
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publisher = "Springer",
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language = "English",
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keywords = "genetic algorithms, genetic programming, Postfix
genetic programming, Symbolic regression, Empirical
modelling, Semantic sub-tree crossover operator",
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isbn13 = "978-81-322-1037-5",
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bibdate = "2013-01-16",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/bic-ta/bic-ta2012-1.html#DabhiC12",
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DOI = "doi:10.1007/978-81-322-1038-2_33",
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abstract = "Design of crossover operator plays a crucial role in
Genetic Programming (GP). The most studied issues
related to crossover operator in GP are: (1) ensuring
that crossover operator always produces syntactically
valid individuals (2) improving search efficiency of
crossover operator. These issues become crucial when
the individuals are represented using linear string
representation. This paper aims to introduce postfix GP
approach to symbolic regression for solving empirical
modelling problems. The main contribution includes (1)
a linear string (postfix notation) based genome
representation method and stack based evaluation to
reduce space-time complexity of GP algorithm (2)
ensuring that sub-tree crossover operator always
produces syntactically valid genomes in linear string
representation (3) using semantic information of
sub-trees, to be swapped, while designing crossover
operator for linear genome representation to provide
additional search guidance. The proposed method is
tested on two real valued symbolic regression problems.
Two different constant creation techniques for Postfix
GP, one that explicitly use list of constants and
another without use of the list, are presented to
evolve useful numeric constants for symbolic regression
problems. The results on tested problems show that
postfix GP comprised of semantic sub-tree crossover
offers a new possibility for efficiently solving
empirical modelling problems.",
- }
Genetic Programming entries for
Vipul K Dabhi
Sanjay Chaudhary
Citations