Complexity-based fitness evaluation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InCollection{Iba:1997:HEC,
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author = "Hitoshi Iba",
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title = "Complexity-based fitness evaluation",
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booktitle = "Handbook of Evolutionary Computation",
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publisher = "Oxford University Press",
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publisher_2 = "Institute of Physics Publishing",
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year = "1997",
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editor = "Thomas Baeck and David B. Fogel and
Zbigniew Michalewicz",
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chapter = "section C4.4",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7503-0392-1",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
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DOI = "doi:10.1201/9781420050387.ptc",
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size = "8 pages",
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abstract = "This section describes the complexity-based fitness
evaluation for evolutionary algorithms. We first
introduce and compare the leading competing model
selection criteria, namely, an MDL
(minimum-description-length) principle, the AIC (Akaike
information criterion), an MML (minimum-message-length)
principle, the PLS (predictive least-squares) measure,
cross-validation, and the maximum-entropy principle.
Then we give an illustrative example to show the
effectiveness of the complexity-based fitness by
experimenting with evolving decision trees using
genetic programming (GP). Thereafter, we describe
various research on complexity-based fitness
evaluation, that is, controlling genetic algorithm or
GP search strategies by means of the MDL criterion.",
- }
Genetic Programming entries for
Hitoshi Iba
Citations