Learning to Control the Program Evolution Process with Cultural Algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{zannoni:1997:lcpepca,
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author = "Elena Zannoni and Robert G. Reynolds",
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title = "Learning to Control the Program Evolution Process with
Cultural Algorithms",
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journal = "Evolutionary Computation",
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year = "1997",
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volume = "5",
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number = "2",
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pages = "181--211",
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month = "summer",
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keywords = "genetic algorithms, genetic programming, cultural
algorithms, software design methodologies, software
metrics, machine learning of software design concepts,
design concept reuse",
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ISSN = "1063-6560",
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URL = "http://www.mitpressjournals.org/doi/pdf/10.1162/evco.1997.5.2.181",
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DOI = "doi:10.1162/evco.1997.5.2.181",
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size = "31 pages",
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abstract = "Traditional software engineering dictates the use of
modular and structured programming and top-down
stepwise refinement techniques that reduce the amount
of variability arising in the development process by
establishing standard procedures to be followed while
writing software. This focusing leads to reduced
variability in the resulting products, due to the use
of standardized constructs. Genetic programming (GP)
performs heuristic search in the space of programs.
Programs produced through the GP paradigm emerge as the
result of simulated evolution and are built through a
bottom-up process, incrementally augmenting their
functionality until a satisfactory level of performance
is reached. Can we automatically extract knowledge from
the GP programming process that can be useful to focus
the search and reduce product variability, thus leading
to a more effective use of the available resources? An
answer to this question is investigated with the aid of
cultural algorithms. A new system has two levels. The
first is the pool of genetic programs (population
level), and the second is a knowledge repository
(belief set) that is built during the GP run and is
used to guide the search process. The microevolution
within the population brings about potentially
meaningful characteristics of the programs for the
achievement of the given task, such as properties
exhibited by the best performers in the population.
CAGP extracts these features and represents them as the
set of the current beliefs. Beliefs correspond to
constraints that all the genetic operators and programs
must follow. Interaction between the two levels occurs
in one direction through the extraction process and, in
the other, through the modulation of an individual's
program parameters according to which, and how many, of
the constraints it follows. CAGP is applied to solve an
instance of the symbolic regression problem, in which a
function of one variable needs to be discovered. The
results of the experiments show an overall improvement
on the average performance of CAGP over GP alone and a
significant reduction of the complexity of the produced
solution. Moreover, the execution time required by CAGP
is comparable with the time required by GP alone.",
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notes = "Evolutionary Computation (Journal)
Special Issue: Trends in Evolutionary Methods for
Program Induction PMID: 10021758
Cited by \cite{ostrowski:1998:ismcaoalsesd}",
- }
Genetic Programming entries for
Elena Zannoni
Robert G Reynolds
Citations