Multi-Objective Genetic Programming Based Design of Fuzzy Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Freischlad:2005:ICCCE,
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author = "M. Freischlad and M. Schnellenbach-Held",
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title = "Multi-Objective Genetic Programming Based Design of
Fuzzy Systems",
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booktitle = "Proceedings of the 2005 ASCE International Conference
on Computing in Civil Engineering",
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year = "2005",
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editor = "Lucio Soibelman and Feniosky Pena-Mora",
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address = "Cancun, Mexico",
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month = jul # " 12-15",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1061/40794(179)62",
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abstract = "The Multi-Objective Domain Knowledge Augmented Genetic
Fuzzy System (MODA-GFS) is a GP based fuzzy system for
the data-driven generation of fuzzy rule based systems.
The algorithm incorporates domain specific knowledge
that is used by human knowledge engineers in the manual
fuzzy system design process. The combination of
characteristics of two individuals is most interesting
if both individuals complement each other. In terms of
fuzzy systems this means a potential crossover partner
(parent B) has a lower approximation error in an area
of the input space, where parent A has a higher error.
Within MODA-GFS a method for the determination of
feasible crossover mates is implemented. In addition
MODA-GFS includes a method for the goal-oriented
selection of parent rules that are handed down to the
offspring. Especially in the domain of knowledge
representation the quality of a fuzzy system is not
only determined by its approximation capability but
also by its transparency. In order to assure the
automated generation of fuzzy systems that are both
accurate and transparent multi-objective optimisation
methods are implemented. Tests carried out on test
functions as well as on real world data sets have shown
that the incorporation of domain knowledge
significantly speeds up the evolution process. Besides
these test results the integration and application of
the new methods for automated generation of fuzzy
models within a learning expert system environment are
described in this paper. Finally an outlook on the
current and future work is given, i.e. the transfer of
the presented findings to the evolutionary optimisation
of large-scale structures.",
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notes = "c2005 ASCE",
- }
Genetic Programming entries for
Mark Freischlad
Martina Schnellenbach-Held
Citations