An empirical study of the efficiency of learning Boolean functions using a Cartesian Genetic Programming approach
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- @InProceedings{miller:1999:ACGP,
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author = "Julian F. Miller",
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title = "An empirical study of the efficiency of learning
{Boolean} functions using a Cartesian Genetic
Programming approach",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "1999",
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editor = "Wolfgang Banzhaf and Jason Daida and
Agoston E. Eiben and Max H. Garzon and Vasant Honavar and
Mark Jakiela and Robert E. Smith",
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volume = "2",
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pages = "1135--1142",
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address = "Orlando, Florida, USA",
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publisher_address = "San Francisco, CA 94104, USA",
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month = "13-17 " # jul,
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publisher = "Morgan Kaufmann",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, evolvable hardware",
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ISBN = "1-55860-611-4",
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URL = "http://citeseer.ist.psu.edu/153431.html",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-411.ps",
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URL = "https://dl.acm.org/doi/10.5555/2934046.2934074",
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abstract = "A new form of Genetic Programming (GP) called
Cartesian Genetic Programming (CGP) is proposed in
which programs are represented by linear integer
chromosomes in the form of connections and
functionalities of a rectangular array of primitive
functions. The effectiveness of this approach is
investigated for boolean even-parity functions (3,4,5),
and the 2-bit multiplier. The minimum number of
evaluations required to give a 0.99 probability of
evolving a target function is used to measure the
efficiency of the new approach. It is found that
extremely low populations are most effective. A simple
probabilistic hillclimber (PH) is devised which proves
to be even more effective. For these boolean functions
either method appears to be much more efficient than
the GP and Evolutionary Programming (EP) methods
reported. The efficacy of the PH suggests that boolean
function learning may not be an appropriate problem for
testing the effectiveness of GP and EP.",
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notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
- }
Genetic Programming entries for
Julian F Miller
Citations