ACGP: Adaptable Constrained Genetic Programming
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- @InCollection{janikow:2004:GPTP,
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author = "Cezary Z. Janikow",
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title = "{ACGP}: Adaptable Constrained Genetic Programming",
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booktitle = "Genetic Programming Theory and Practice {II}",
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year = "2004",
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editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
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chapter = "12",
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pages = "191--206",
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address = "Ann Arbor, MI, USA",
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month = "13-15 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming,
representation, learning, adaptation, heuristics",
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ISBN = "0-387-23253-2",
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URL = "http://www.umsl.edu/cmpsci/about/People/Faculty/CezaryJanikow/untitled%20folder/ACGP.pdf",
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DOI = "doi:10.1007/0-387-23254-0_12",
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abstract = "Genetic Programming requires that all
functions/terminals (tree labels) be given a priori. In
the absence of specific information about the solution,
the user is often forced to provide a large set, thus
enlarging the search space often resulting in reducing
the search efficiency. Moreover, based on heuristics,
syntactic constraints, or data typing, a given subtree
may be undesired or invalid in a given context. Typed
Genetic Programming methods give users the power to
specify some rules for valid tree construction, and
thus to prune the otherwise unconstrained
representation in which Genetic Programming operates.
However, in general, the user may not be aware of the
best representation space to solve a particular
problem. Moreover, some information may be in the form
of weak heuristics. In this work, we present a
methodology, which automatically adapts the
representation for solving a particular problem, by
extracting and using such heuristics. Even though many
specific techniques can be implemented in the
methodology, in this paper we use information on local
first-order (parent-child) distributions of the
functions and terminals. The heuristics are extracted
from the population by observing their distribution in
better individuals. The methodology is illustrated and
validated using a number of experiments with the
11-multiplexer. Moreover, some preliminary empirical
results linking population size and the sampling rate
are also given.",
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notes = "part of \cite{oreilly:2004:GPTP2}",
- }
Genetic Programming entries for
Cezary Z Janikow
Citations