Using Multi-objective Genetic Programming to Synthesize Stochastic Processes
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Ross:2009:GPTP,
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author = "Brian J. Ross and Janine Imada",
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title = "Using Multi-objective Genetic Programming to
Synthesize Stochastic Processes",
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booktitle = "Genetic Programming Theory and Practice {VII}",
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year = "2009",
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editor = "Rick L. Riolo and Una-May O'Reilly and
Trent McConaghy",
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series = "Genetic and Evolutionary Computation",
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address = "Ann Arbor",
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month = "14-16 " # may,
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publisher = "Springer",
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chapter = "10",
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pages = "159--175",
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keywords = "genetic algorithms, genetic programming, stochastic
processes, process algebra, time-series feature tests,
multi-objective gp, MOGP",
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isbn13 = "978-1-4419-1653-2",
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DOI = "doi:10.1007/978-1-4419-1626-6_10",
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abstract = "Genetic programming is used to automatically construct
stochastic processes written in the stochastic
pi-calculus. Grammar-guided genetic programming
constrains search to useful process algebra structures.
The time-series behaviour of a target process is
denoted with a suitable selection of statistical
feature tests. Feature tests can permit complex process
behaviours to be effectively evaluated. However, they
must be selected with care, in order to accurately
characterise the desired process behaviour.
Multi-objective evaluation is shown to be appropriate
for this application, since it permits heterogeneous
statistical feature tests to reside as independent
objectives. Multiple undominated solutions can be saved
and evaluated after a run, for determination of those
that are most appropriate. Since there can be a vast
number of candidate solutions, however, strategies for
filtering and analysing this set are required.",
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notes = "part of \cite{Riolo:2009:GPTP}",
- }
Genetic Programming entries for
Brian J Ross
Janine H Imada
Citations