Scalable learning in genetic programming using automatic function definition
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InCollection{Kinnear:Koza:1994:adf,
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author = "John R. Koza",
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title = "Scalable learning in genetic programming using
automatic function definition",
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booktitle = "Advances in Genetic Programming",
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publisher = "MIT Press",
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year = "1994",
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editor = "Kenneth E. {Kinnear, Jr.}",
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pages = "99--117",
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chapter = "5",
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address = "Cambridge, MA, USA",
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keywords = "genetic algorithms, genetic programming",
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URL = "
http://www.genetic-programming.com/jkpdf/aigp1994lawn.pdf",
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URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap5.pdf",
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DOI = "
doi:10.7551/mitpress/1108.003.0010",
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size = "20 pages",
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abstract = "This chapter uses three differently sized versions of
an illustrative problem that has considerable
regularity, symmetry, and homogeneity in its problem
environment to compare genetic programming with and
without the newly developed mechanism of automatic
function definition. Genetic programming with automatic
function definition can automatically decompose a
problem into simpler subproblems, solve the
subproblems, and assemble the solutions to the
subproblems into a solution to the original overall
problem. The solutions to the problem produced by
genetic programming with automatic function definition
are more parsimonious than those produced without it.
Genetic programming requires fewer fitness evaluations
to yield a solution to the problem with 99percent
probability with automatic function definition than
without it. When we consider the three differently
sized versions of the problem we find that the size of
the solutions produced without automatic function
definition can be expressed as a direct multiple of
problem size. In contrast, the average size of
solutions with automatic function definition is
expressed as a certain minimum size representing the
overhead associated with automatic function definition;
however, there is only a very slight increase in the
average size of the solutions with problem size.
Moreover, the number of fitness evaluations required to
yield a solution to the problem with a 99percent
probability grows very rapidly with problem size
without automatic function definition, but this same
measure grows only linearly with problem size with
automatic function definition.",
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notes = "Part of \cite{kinnear:book}",
- }
Genetic Programming entries for
John Koza
Citations
