Adaptable Constrained Genetic Programming: Extensions and Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@TechReport{janikow:2004:NASA,
  author =       "Cezary Z. Janikow",
  title =        "Adaptable Constrained Genetic Programming: Extensions
                 and Applications",
  institution =  "NASA",
  year =         "2005",
  type =         "Summer Faculty Fellowship Program 2004",
  number =       "Volumes 1 and 2, Page: 11-1 - 11-7",
  month =        "1 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  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.",
  notes =        "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)",
  size =         "7 pages",
}

Genetic Programming entries for Cezary Z Janikow

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