A (mu + lambda) - GP Algorithm and its use for Regression Problems
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gp-bibliography.bib Revision:1.8098
- @InProceedings{Costa:2006:ICTAI,
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author = "Eduardo Oliveira Costa and Aurora Pozo",
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title = "A (mu + lambda) - {GP} Algorithm and its use for
Regression Problems",
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booktitle = "8th IEEE International Conference on Tools with
Artificial Intelligence, ICTAI '06",
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year = "2006",
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pages = "10--17",
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address = "Arlington, VA, USA",
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month = "13-15 " # nov,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7695-2728-0",
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DOI = "doi:10.1109/ICTAI.2006.6",
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size = "8 pages",
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abstract = "The genetic programming (GP) is a powerful technique
for symbolic regression. However, because it is a new
area, many improvements can be obtained changing the
basic behaviour of the method. In this way, this work
develop a different genetic programming algorithm doing
some modifications on the classical GP algorithm and
adding some concepts of evolution strategies. The new
approach was evaluated using two instances of symbolic
regression problem - the binomial-3 problem (a tunably
difficult problem), proposed in (J.M. Daida et al.,
2001) and the problem of modelling software reliability
growth (an application of symbolic regression). The
discovered results were compared with the classical GP
algorithm. The symbolic regression problems obtained
excellent results and an improvement was detected using
the proposed approach",
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notes = "Dept. of Comput. Sci., Fed. Univ. of Parana,
Curitiba",
- }
Genetic Programming entries for
Eduardo Oliveira Costa
Aurora Trinidad Ramirez Pozo
Citations