An Empirical Study of the GPP Accelerating Phenomenon
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- @InProceedings{cheang:2003:CIRAS,
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author = "Sin Man Cheang",
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title = "An Empirical Study of the {GPP} Accelerating
Phenomenon",
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booktitle = "Proceedings of the second International Conference on
Computational Intelligence, Robotics and Autonomous
Systems -- CIRAS-2003",
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year = "2003",
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editor = "P. Vadakkepat and T. W. Wan and T. K. Chen and
L. A. Poh",
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pages = "PS04--4--03",
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address = "Singapore",
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month = "15-18 " # dec,
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organisation = "Centre for Intelligent Control, National Univ. of
Singapore",
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publisher = "National Univ. of Singapore",
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keywords = "genetic algorithms, genetic programming",
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abstract = "The Genetic Parallel Programming (GPP) is a novel
Linear-structure Genetic Programming paradigm that
learns parallel programs. We discover the GPP
Accelerating Phenomenon, i.e. parallel programs are
evolved faster than their counterpart sequential
programs of identical functions. This paper presents an
empirical study of Boolean function regression based on
a Multi-ALU Processor that results in the phenomenon.
We performed a series of random search experiments
using different numbers of ALUs (w) and instructions
(l). We identify that w (the degree of parallelism of
the program) is the dominant factor that affects the
searching performance. In a 3-input Boolean function
experiment, searching a single-ALU program requires 875
times on average of the computational effort of an
8-ALU program. An investigation on the probabilities of
finding solutions to different problem instances shows
that parallel representation of programs can increase
the probabilities of finding solutions to hard
problems.",
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notes = "http://ciras.nus.edu.sg/2003/Proceedings/ProgramDec17.pdf",
- }
Genetic Programming entries for
Ivan Sin Man Cheang
Citations