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Adaptable Constrained Genetic Programming: Extensions and ApplicationsAn 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.
Document ID
20050202032
Acquisition Source
Johnson Space Center
Document Type
Other
Authors
Janikow, Cezary Z. (Missouri Univ. Saint Louis, MO, United States)
Date Acquired
September 7, 2013
Publication Date
August 1, 2005
Publication Information
Publication: NASA Summer Faculty Fellowship Program 2004, Volumes 1 and 2
Subject Category
Life Sciences (General)
Funding Number(s)
CONTRACT_GRANT: NAG9-1526
CONTRACT_GRANT: NNJ04JF93A
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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IDRelationTitle20050202011Collected WorksNASA Summer Faculty Fellowship Program 2004, Volumes 1 and 2
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