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1Genetic Programming, Proceedings of EuroGP'2001
J. Miller, M. Tomassini, P. Lanzi, C. Ryan, A. Tettamanzi, and W. Langdon (Eds.) volume 2038 of LNCS, Lake Como, Italy, Springer-Verlag, (18-20 April 2001)
17 years ago by @brazovayeye
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Active
Animat,
Arm,
Artificial
BDD,
Bloat,
Block-oriented
Boolean
CAD,
Causality,
Cellular
Character
Complexity,
Computational
Contour
Controller
Crossover
DNA,
Developmental
Discovery,
Dynamic
Evolution
Evolution,
Evolvability,
Evolvable
Extraction,
Feature
Fitness,
Fixed
Function
Generator,
Genetic
Genotype-Phenotype
Grammatical
Hardware,
Image
Intrinsic
Inverse
Iterated
Kinematics,
Knowledge
Layered
Learning,
Length
Linear
MAX
Machine
Mapping,
Multi-expression
Multipopulation
Neutral
One-then-zeros
Parallel
Pattern
Polymorphism,
Problem
Problem,
Process
Program
Reasoning,
Recognition,
Regression,
Retina,
Robotic
STGP,
Series
Symbolic
Systems,
Time
Trees,
Turing
VHDL,
VLSI
algorithms,
bias,
control,
design,
detection,
distributed
distributions,
function
genetic
individuals,
landscape,
machines,
model,
modelling,
modular
mutation,
of
points,
prediction,
problem,
processing,
programming,
representation,
representations,
robot,
robust,
size,
structures,
ActiveAnimat,Arm,ArtificialBDD,Bloat,Block-orientedBooleanCAD,Causality,CellularCharacterComplexity,ComputationalContourControllerCrossoverDNA,DevelopmentalDiscovery,DynamicEvolutionEvolution,Evolvability,EvolvableExtraction,FeatureFitness,FixedFunctionGenerator,GeneticGenotype-PhenotypeGrammaticalHardware,ImageIntrinsicInverseIteratedKinematics,KnowledgeLayeredLearning,LengthLinearMAXMachineMapping,Multi-expressionMultipopulationNeutralOne-then-zerosParallelPatternPolymorphism,ProblemProblem,ProcessProgramReasoning,Recognition,Regression,Retina,RoboticSTGP,SeriesSymbolicSystems,TimeTrees,TuringVHDL,VLSIalgorithms,bias,control,design,detection,distributeddistributions,functiongeneticindividuals,landscape,machines,model,modelling,modularmutation,ofpoints,prediction,problem,processing,programming,representation,representations,robot,robust,size,structures,
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1Geometry Reconstruction of Conducting Cylinders Using Genetic Programming
R. Wildman, and D. Weile. IEEE Transactions on Antennas and Propagation, 55 (3): 629--636 (March 2007)
17 years ago by @brazovayeye
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Boolean
algorithms,
bodies,
chromosome,
combination,
computational
conducting
conductors
cylinder,
electromagnetic
electromagnetics,
encoding,
functions,
genetic
geometry
geometry,
inverse
problem,
programming,
reconstruction,
scattering
scattering,
tree-shaped
two-dimensional
wave
Booleanalgorithms,bodies,chromosome,combination,computationalconductingconductorscylinder,electromagneticelectromagnetics,encoding,functions,geneticgeometrygeometry,inverseproblem,programming,reconstruction,scatteringscattering,tree-shapedtwo-dimensionalwave
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1Using Domain Knowledge in Evolutionary System Identification
M. Schoenauer, and M. Sebag. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, (19-21 September 2001)
17 years ago by @brazovayeye
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algorithms,
genetic
inverse
problems
programming,
algorithms,geneticinverseproblemsprogramming,
(0)
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2Evolving Hand-Eye Coordination for a Humanoid Robot with Machine Code Genetic Programming
W. Langdon, and P. Nordin. Genetic Programming, Proceedings of EuroGP'2001, volume 2038 of LNCS, page 313--324. Lake Como, Italy, Springer-Verlag, (18-20 April 2001)
17 years ago by @brazovayeye
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Arm,
Brain
Building,
Discipulus
Genetic
Humanoid
Inverse
Kinematics,
Reasoning,
Robotic
Robotics,
Robots,
Stereo
Vision,
algorithms,
genetic
programming,
Arm,BrainBuilding,DiscipulusGeneticHumanoidInverseKinematics,Reasoning,RoboticRobotics,Robots,StereoVision,algorithms,geneticprogramming,
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2Application of genetic programming to the calibration of industrial robots
J. Dolinsky, I. Jenkinson, and G. Colquhoun. Computers in Industry, 58 (3): 255--264 (April 2007)
17 years ago by @brazovayeye
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Co-evolution
Distal
Inverse
algorithms,
calibration,
genetic
kinematic
learning,
programming,
static
supervised
Co-evolutionDistalInversealgorithms,calibration,genetickinematiclearning,programming,staticsupervised
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2Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
P. Collet, E. Lutton, F. Raynal, and M. Schoenauer. Genetic Programming and Evolvable Machines, 1 (4): 339--361 (October 2000)
17 years ago by @brazovayeye
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Functions
IFS
IFS,
Iterated
System,
algorithms,
for
fractals,
genetic
inverse
polar
problem
programming,
FunctionsIFSIFS,IteratedSystem,algorithms,forfractals,geneticinversepolarproblemprogramming,
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1Closed form solutions for inverse kinematics approximation of general 6R manipulators
F. Chapelle, and P. Bidaud. Mechanism and Machine Theory, 39 (3): 323--338 (March 2004)
17 years ago by @brazovayeye
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Genetic
Inverse
Manipulators,
Mechanical
Symbolic
algorithms,
design,
genetic
kinematics,
programming,
regression
GeneticInverseManipulators,MechanicalSymbolicalgorithms,design,genetickinematics,programming,regression
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1Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires
F. Chapelle. University Pierre et Marie Curie, Paris VI, France, (September 2002)in french.
17 years ago by @brazovayeye
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Computer-aided
algorithm,
control,
design,
devices,
evaluation,
genetic
invasive
inverse
kinematic
medical
minimally
models,
problem,
programming,
regression,
robotic
simulation,
simultaneous
structure/control
surgery
symbolic
systems,
Computer-aidedalgorithm,control,design,devices,evaluation,geneticinvasiveinversekinematicmedicalminimallymodels,problem,programming,regression,roboticsimulation,simultaneousstructure/controlsurgerysymbolicsystems,
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2A closed form for inverse kinematics approximation of general 6R manipulators using genetic programming
F. Chapelle, and P. Bidaud. IEEE International Conference on Robotics and Automation (ICRA'01), 4, page 3364--3369. Seoul, Korea, IEEE, (21-28 May 2001)
17 years ago by @brazovayeye
show all tags
6R
ADF,
algorithms,
approximation,
base
data,
demes,
evolutionary
genetic
industrial
inverse
joint
kinematics,
learning
manipulation,
manipulator
manipulators,
parsimony
preasure,
programming,
regression,
state,
steady
subsample
symbol
symbolic
training
variables,
6RADF,algorithms,approximation,basedata,demes,evolutionarygeneticindustrialinversejointkinematics,learningmanipulation,manipulatormanipulators,parsimonypreasure,programming,regression,state,steadysubsamplesymbolsymbolictrainingvariables,
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publications (hide)9
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1Genetic Programming, Proceedings of EuroGP'2001

1Geometry Reconstruction of Conducting Cylinders Using Genetic Programming

1Using Domain Knowledge in Evolutionary System Identification

2Evolving Hand-Eye Coordination for a Humanoid Robot with Machine Code Genetic Programming

2Application of genetic programming to the calibration of industrial robots

2Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving

1Closed form solutions for inverse kinematics approximation of general 6R manipulators

1Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires

2A closed form for inverse kinematics approximation of general 6R manipulators using genetic programming

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