Towards Automatic Grammatical Evolution for Real-world Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{conf/ijcci/Ali0NR21,
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author = "Muhammad Sarmad Ali and Meghana Kshirsagar and
Enrique Naredo and Conor Ryan",
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title = "Towards Automatic Grammatical Evolution for Real-world
Symbolic Regression",
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booktitle = "Proceedings of the 13th International Joint Conference
on Computational Intelligence, IJCCI",
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year = "2021",
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editor = "Thomas Baeck and Christian Wagner and
Jonathan M. Garibaldi and H. K. Lam and Marie Cottrell and
Juan Julian Merelo and Kevin Warwick",
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pages = "68--78",
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address = "Online",
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month = oct # " 25-27",
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organization = "INSTICC",
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publisher = "SCITEPRESS",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, grammar pruning, effective genome length",
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isbn13 = "978-989-758-534-0",
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bibdate = "2021-11-19",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ijcci/ijcci2021.html#Ali0NR21",
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DOI = "doi:10.5220/0010691500003063",
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size = "11 pages",
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abstract = "AutoGE (Automatic Grammatical Evolution) is a tool
designed to aid users of GE for the automatic
estimation of Grammatical Evolution (GE) parameters, a
key one being the grammar. The tool comprises of a rich
suite of algorithms to assist in fine tuning a BNF
(Backus-Naur Form) grammar to make it adaptable across
a wide range of problems. It primarily facilitates the
identification of better grammar structures and the
choice of function sets to enhance existing fitness
scores at a lower computational overhead. we discuss
and report experimental results for our Production Rule
Pruning algorithm from AutoGE which employs a simple
frequency-based approach for eliminating less useful
productions. It captures the relationship between
production rules and function sets involved in the
problem domain to identify better grammar. The
experimental study incorporates an extended function
set and common grammar structures for grammar
definition. Preliminary results based on ten popular
real-world regression datasets demonstrate that the
proposed algorithm not only identifies suitable grammar
structures, but also prunes the grammar which results
in shorter genome length for every problem, thus
optimising memory usage. Despite using a fraction of
budget in pruning, AutoGE was able to significantly
enhance test scores for 3 problems.",
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notes = "Biocomputing and Developmental Systems Lab, University
of Limerick, Ireland",
- }
Genetic Programming entries for
Muhammad Sarmad Ali
Meghana Kshirsagar
Enrique Naredo
Conor Ryan
Citations