A Self-Scaling Instruction Generator Using Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Liu:2011:EuroGP,
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author = "Yang Liu and Gianluca Tempesti and James A. Walker and
Jon Timmis and Andrew M. Tyrrell and Paul Bremner",
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title = "A Self-Scaling Instruction Generator Using Cartesian
Genetic Programming",
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booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
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year = "2011",
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month = "27-29 " # apr,
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editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
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series = "LNCS",
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volume = "6621",
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publisher = "Springer Verlag",
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address = "Turin, Italy",
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pages = "298--309",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming: poster",
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isbn13 = "978-3-642-20406-7",
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DOI = "doi:10.1007/978-3-642-20407-4_26",
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abstract = "In the past decades, a number of genetic programming
techniques have been developed to evolve machine
instructions. However, these approaches typically
suffer from a lack of scalability that seriously
impairs their applicability to real-world scenarios. In
this paper, a novel self-scaling instruction generation
method is introduced, which tries to overcome the
scalability issue by using Cartesian Genetic
Programming. In the proposed method, a dual-layer
network architecture is created: one layer is used to
evolve a series of instructions while the other is
dedicated to the generation of loop control
parameters.",
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notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
- }
Genetic Programming entries for
Yang Liu
Gianluca Tempesti
James Alfred Walker
Jon Timmis
Andrew M Tyrrell
Paul Bremner
Citations