A parallel implementation of genetic programming that achieves super-linear performance
Created by W.Langdon from
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- @Article{AK97,
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author = "David Andre and John R. Koza",
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title = "A parallel implementation of genetic programming that
achieves super-linear performance",
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journal = "Information Sciences",
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year = "1998",
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volume = "106",
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number = "3-4",
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pages = "201--218",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0020-0255",
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URL = "
http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-21/2/22b9842f820b08883990bbae1d889c03",
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URL = "
http://www.davidandre.com/papers/isj97.ps",
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DOI = "
doi:10.1016/S0020-0255(97)10011-1",
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size = "18 pages",
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abstract = "This paper describes the successful parallel
implementation of genetic programming on a network of
processing nodes using the transputer architecture.
With this approach, researchers of genetic algorithms
and genetic programming can acquire computing power
that is intermediate between the power of currently
available workstations and that of supercomputers at
intermediate cost. This approach is illustrated by a
comparison of the computational effort required to
solve a benchmark problem. Because of the decoupled
character of genetic programming, our approach achieved
a nearly linear speed up from parallelization. In
addition, for the best choice of parameters tested, the
use of subpopulations delivered a super-linear
speed-up in terms of the ability of the algorithm to
solve the problem. Several examples are also presented
where the parallel genetic programming system evolved
solutions that are competitive with human
performance.",
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notes = "Information Sciences
http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
- }
Genetic Programming entries for
David Andre
John Koza
Citations