Neuro-guided genetic programming: prioritizing evolutionary search with neural networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Liskowski:2018:GECCOa,
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author = "Pawel Liskowski and Iwo Bladek and Krzysztof Krawiec",
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title = "Neuro-guided genetic programming: prioritizing
evolutionary search with neural networks",
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booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2018",
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editor = "Hernan Aguirre and Keiki Takadama and
Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and
Andrew M. Sutton and Satoshi Ono and Francisco Chicano and
Shinichi Shirakawa and Zdenek Vasicek and
Roderich Gross and Andries Engelbrecht and Emma Hart and
Sebastian Risi and Ekart Aniko and Julian Togelius and
Sebastien Verel and Christian Blum and Will Browne and
Yusuke Nojima and Tea Tusar and Qingfu Zhang and
Nikolaus Hansen and Jose Antonio Lozano and
Dirk Thierens and Tian-Li Yu and Juergen Branke and
Yaochu Jin and Sara Silva and Hitoshi Iba and
Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and
Federica Sarro and Giuliano Antoniol and Anne Auger and
Per Kristian Lehre",
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isbn13 = "978-1-4503-5618-3",
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pages = "1143--1150",
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address = "Kyoto, Japan",
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DOI = "doi:10.1145/3205455.3205629",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, ANN",
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abstract = "When search operators in genetic programming (GP)
insert new instructions into programs, they usually
draw them uniformly from the available instruction set.
Preferring some instructions to others would require
additional domain knowledge, which is typically
unavailable. However, it has been recently demonstrated
that the likelihoods of instructions occurrence in a
program can be reasonably well estimated from its
input-output behaviour using a neural network. We
exploit this idea to bias the choice of instructions
used by search operators in GP. Given a large sample of
programs and their input-output behaviours, a neural
network is trained to predict the presence of
individual instructions. When applied to a new program
synthesis task, the network is first queried on the set
of examples that define the task, and the obtained
probabilities determine the frequencies of using
instructions in initialization and mutation operators.
This priming leads to significant improvements of the
odds of successful synthesis on a range of
benchmarks.",
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notes = "Also known as \cite{3205629} GECCO-2018 A
Recombination of the 27th International Conference on
Genetic Algorithms (ICGA-2018) and the 23rd Annual
Genetic Programming Conference (GP-2018)",
- }
Genetic Programming entries for
Pawel Liskowski
Iwo Bladek
Krzysztof Krawiec
Citations