Genetic Programming Design of Fuzzy Controllers for Mobile Robot Path Tracking
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Homaifar:2000:IJKBIES,
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author = "Abdollah Homaifar and D. Battle and E. Tunstel and
G. Dozier",
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title = "Genetic Programming Design of Fuzzy Controllers for
Mobile Robot Path Tracking",
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journal = "International Journal of Knowledge-Based Intelligent
Engineering Systems",
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year = "2000",
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volume = "4",
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number = "1",
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pages = "33--52",
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month = jan,
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keywords = "genetic algorithms, genetic programming",
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abstract = "Genetic programming (GP) is an evolutionary strategy
that attempts to deal with the notion of how computers
can learn to solve problems without being explicitly
programmed. It has been demonstrated that GP, under the
influence of Darwinian concepts, could genetically
breed computer programs to approximately solve problems
in a variety of applications. One primary example is
its application to the problem of automatically
learning nonlinear mappings that govern the behavior of
control systems. It is demonstrated here that GP can
formulate such nonlinear maps in the form of fuzzy
control rules, which yield comparable or better
performance than one derived through manual design
using trial-and-error. The objective is to address the
efficient implementation of GP for the discovery of
knowledge bases intended for use in fuzzy logic
controller applications. Efficiency is achieved with a
C programming language implementation of GP, which is
applied to a mobile robot steering control problem.
Robot path following performance is compared to results
obtained using an existing GP implementation in the
LISP programming language. It is demonstrated that the
C implementation has a definite advantage with regard
to computational speed of evolution. In this work, we
have extended the application of GP to handle
simultaneous evolution of membership functions and rule
bases for the same control problem. Furthermore, GP is
used to handle selection of fuzzy t-norms. It is
concluded that simultaneous evolution of rule bases and
membership functions with t-norm selection results in
enhanced performance of the evolved controllers.
Finally, the robustness characteristics of the
genetically evolved fuzzy controllers are investigated
by examining the effects of sensor measurement noise
and an increase in the robot's nominal forward
velocity.",
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notes = "Nov 2012 IJKBIES web site not listing stuff before
2004",
- }
Genetic Programming entries for
Abdollah Homaifar
Daryl Battle
Edward W Tunstel
Gerry Dozier
Citations