Adaptable Constrained Genetic Programming: Extensions and Applications
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @TechReport{janikow:2004:NASA,
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author = "Cezary Z. Janikow",
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title = "Adaptable Constrained Genetic Programming: Extensions
and Applications",
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institution = "NASA",
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year = "2005",
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type = "Summer Faculty Fellowship Program 2004",
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number = "Volumes 1 and 2, Page: 11-1 - 11-7",
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month = "1 " # aug,
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keywords = "genetic algorithms, genetic programming, ACGP2.1",
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URL = "http://hdl.handle.net/2060/20050202032",
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URL = "https://ntrs.nasa.gov/api/citations/20050202032/downloads/20050202032.pdf",
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size = "7 pages",
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abstract = "An evolutionary algorithm applies evolution-based
principles to problem solving. To solve a problem, the
user defines the space of potential solutions, the
representation space. Sample solutions are encoded in a
chromosome-like structure. The algorithm maintains a
population of such samples, which undergo simulated
evolution by means of mutation, crossover, and survival
of the fittest principles. Genetic Programming (GP)
uses tree-like chromosomes, providing very rich
representation suitable for many problems of interest.
GP has been successfully applied to a number of
practical problems such as learning Boolean functions
and designing hardware circuits. To apply GP to a
problem, the user needs to define the actual
representation space, by defining the atomic functions
and terminals labeling the actual trees. The
sufficiency principle requires that the label set be
sufficient to build the desired solution trees. The
closure principle allows the labels to mix in any
arity-consistent manner. To satisfy both principles,
the user is often forced to provide a large label set,
with ad hoc interpretations or penalties to deal with
undesired local contexts. This unfortunately enlarges
the actual representation space, and thus usually slows
down the search. In the past few years, three different
methodologies have been proposed to allow the user to
alleviate the closure principle by providing means to
define, and to process, constraints on mixing the
labels in the trees. Last summer we proposed a new
methodology to further alleviate the problem by
discovering local heuristics for building quality
solution trees. A pilot system was implemented last
summer and tested throughout the year. This summer we
have implemented a new revision, and produced a User's
Manual so that the pilot system can be made available
to other practitioners and researchers. We have also
designed, and partly implemented, a larger system
capable of dealing with much more powerful
heuristics.",
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notes = "Broken Aug 2018
http://www.sti.nasa.gov/scan/rss99-01.html Document ID:
20050202032 Report #: None Sales Agency: CASI Hardcopy
A02 No Copyright Source: Missouri Univ. (Saint Louis,
MO, United States)",
- }
Genetic Programming entries for
Cezary Z Janikow
Citations