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1GENETICA: A Computer Language That Supports General Formal Expression With Evolving Data Structures
L. Virirakis. IEEE Transactions on Evolutionary Computation, 7 (5): 456--481 (октября 2003)
17 лет назад , @brazovayeye
CAD,
G-CAD,
GENETICA,
GP,
abstractions,
algorithms,
architectural
artificial
computation,
computer
data
design,
domain
environment
environments,
evolutionary
evolving
experimental
expression,
formal
general
generation
genetic
genotypes,
high
intelligence,
language,
languages,
level
logic,
method,
optimization,
problem
problem-solving
problem-solving,
programming
programming,
results,
scenarios,
solving,
specific
structure,
structures,
CAD,G-CAD,GENETICA,GP,abstractions,algorithms,architecturalartificialcomputation,computerdatadesign,domainenvironmentenvironments,evolutionaryevolvingexperimentalexpression,formalgeneralgenerationgeneticgenotypes,highintelligence,language,languages,levellogic,method,optimization,problemproblem-solvingproblem-solving,programmingprogramming,results,scenarios,solving,specificstructure,structures,
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1Giving Structural Descriptions of Tree-like Objects from Binary Images Using Genetic Programming
R. Vanyi, и G. Kokai. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), стр. 163--172. San Francisco, California, USA, Morgan Kaufmann, (7-11 July 2001)
17 лет назад , @brazovayeye
Lindenmayer
algorithms,
binary
branching,
descriptions,
genetic
image
images,
programming,
reconstruction
structural
structures,
systems,
Lindenmayeralgorithms,binarybranching,descriptions,geneticimageimages,programming,reconstructionstructuralstructures,systems,
(0)
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3Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001
L. Spector, E. Goodman, A. Wu, W. Langdon, H. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. Garzon и 1 other редактор(ы) (Ред.) San Francisco, California, USA, Morgan Kaufmann, (7-11 July 2001)
17 лет назад , @brazovayeye
DNA
adaptive
agents,
algorithms,
and
ant
applications
artificial
behavior,
classifier
colony
computing,
design
engineered
evolution
evolutionary
evolvable
genetic
hardware,
life,
methodology,
molecular
of
optimization,
pedagogy,
philosophy,
programming,
quantum
real
robotics,
routing,
scheduling
strategies,
structures,
systems,
world
DNAadaptiveagents,algorithms,andantapplicationsartificialbehavior,classifiercolonycomputing,designengineeredevolutionevolutionaryevolvablegenetichardware,life,methodology,molecularofoptimization,pedagogy,philosophy,programming,quantumrealrobotics,routing,schedulingstrategies,structures,systems,world
(0)
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2Regularization Approach to Inductive Genetic Programming
N. Nikolaev, и H. Iba. IEEE Transactions on Evolutionary Computing, 54 (4): 359--375 (августа 2001)
17 лет назад , @brazovayeye
(artificial
Kolmogorov-Gabor
STROGANOFF,local
algorithms,
bias,
data
genetic
inductive
intelligence),
learning
mining,
multivariate
nodes,
polynomials,
prediction,
programming,
regularization,
search
searching,
series
statistical
structures,
time
tree
(artificialKolmogorov-GaborSTROGANOFF,localalgorithms,bias,datageneticinductiveintelligence),learningmining,multivariatenodes,polynomials,prediction,programming,regularization,searchsearching,seriesstatisticalstructures,timetree
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1Genetic Programming, Proceedings of EuroGP'2001
J. Miller, M. Tomassini, P. Lanzi, C. Ryan, A. Tettamanzi, и W. Langdon (Ред.) том 2038 из LNCS, Lake Como, Italy, Springer-Verlag, (18-20 April 2001)
17 лет назад , @brazovayeye
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,
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detection,
distributed
distributions,
function
genetic
individuals,
landscape,
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mutation,
of
points,
prediction,
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processing,
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representations,
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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,
(0)
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1Evolutionary synthesis of digital filter structures using genetic programming
K. Uesaka, и M. Kawamata. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 50 (12): 977--983 (декабря 2003)
17 лет назад , @brazovayeye
IIR
S-expressions,
algorithms,
automatically
computability,
defined
difference
digital
effects,
equations,
errors,
evolutionary
filter
filter,
filters,
fitness
fourth-order
function
functions,
genetic
global
infinite-impulse
low-coefficient
low-output
matrices,
matrix
measure,
method,
noise,
optimization,
programming,
representation,
response
roundoff
sensitivity,
structures,
subroutines,
synthesis
transfer
wordlength
IIRS-expressions,algorithms,automaticallycomputability,defineddifferencedigitaleffects,equations,errors,evolutionaryfilterfilter,filters,fitnessfourth-orderfunctionfunctions,geneticglobalinfinite-impulselow-coefficientlow-outputmatrices,matrixmeasure,method,noise,optimization,programming,representation,responseroundoffsensitivity,structures,subroutines,synthesistransferwordlength
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1A Probabilistic Model of Size Drift
J. Rosca. Genetic Programming Theory and Practice, глава 8, Kluwer, (2003)
17 лет назад , @brazovayeye
Evolutionary
algorithms
algorithms,
and
code
code,
complexity
diffusion,
drift,
effect,
effective
fitness
fitness-size
genetic
growth,
introns,
landscapes
model,
noneffective
programming,
rooted-tree
schemata,
size
small
structures,
variable-size
with
Evolutionaryalgorithmsalgorithms,andcodecode,complexitydiffusion,drift,effect,effectivefitnessfitness-sizegeneticgrowth,introns,landscapesmodel,noneffectiveprogramming,rooted-treeschemata,sizesmallstructures,variable-sizewith
(0)
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1ptGAs--Genetic Algorithms Evolving Noncoding Segments by Means of Promoter/Terminator Sequences
H. Mayer. Evolutionary Computation, 6 (4): 361--386 (Winter 1998)
17 лет назад , @brazovayeye
algorithms,
chromosome
combinatorial
crossover,
genetic
noncoding
optimization.
promoter/terminator
segments,
sequences,
spontaneous
structures,
algorithms,chromosomecombinatorialcrossover,geneticnoncodingoptimization.promoter/terminatorsegments,sequences,spontaneousstructures,
(0)
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2Using Data Structures within Genetic Programming
W. Langdon. Genetic Programming 1996: Proceedings of the First Annual Conference, стр. 141--148. Stanford University, CA, USA, MIT Press, (28--31 July 1996)
17 лет назад , @brazovayeye
(ADF),
Artificial
Automatic
Automatically
CFG,
Data
Defined
Demes
Dyck
Evolution,
Expressions,
Functions
Learning,
Machine
Object
Oriented
Pareto
Polish
Programming,
Push
Reverse
Stack,
Structures,
algorithms,
automatic
brackets,
code
context
down
fitness,
free
generation,
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grammar
induction,
language
language,
matched
programming,
(ADF),ArtificialAutomaticAutomaticallyCFG,DataDefinedDemesDyckEvolution,Expressions,FunctionsLearning,MachineObjectOrientedParetoPolishProgramming,PushReverseStack,Structures,algorithms,automaticbrackets,codecontextdownfitness,freegeneration,geneticgrammarinduction,languagelanguage,matchedprogramming,
(0)
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2Using Data Structures within Genetic Programming
W. Langdon. Research Note, RN/96/1. UCL, Gower Street, London, WC1E 6BT, UK, (января 1996)
17 лет назад , @brazovayeye
(ADF),
Artificial
Automatic
Automatically
Data
Defined
Demes
Evolution,
Expressions,
Functions
Learning,
Machine
Object
Oriented
Pareto
Polish
Programming,
Push
Reverse
Stack,
Structures,
algorithms,
automatic
code
context
down
fitness,
free
generation,
genetic
induction,
language
programming,
(ADF),ArtificialAutomaticAutomaticallyDataDefinedDemesEvolution,Expressions,FunctionsLearning,MachineObjectOrientedParetoPolishProgramming,PushReverseStack,Structures,algorithms,automaticcodecontextdownfitness,freegeneration,geneticinduction,languageprogramming,
(0)
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2Evolving Data Structures Using Genetic Programming
W. Langdon. Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), стр. 295--302. Pittsburgh, PA, USA, Morgan Kaufmann, (15-19 July 1995)
17 лет назад , @brazovayeye
(ADF),
(FIFO)
Artificial
Automatic
Automatically
Data
Defined
Demes
Evolution,
First-in
Functions
Learning,
Machine
Object
Oriented
Pareto
Programming,
Push
Queue,
Stack,
Structures,
algorithms,
down
first-out
fitness,
genetic
programming,
(ADF),(FIFO)ArtificialAutomaticAutomaticallyDataDefinedDemesEvolution,First-inFunctionsLearning,MachineObjectOrientedParetoProgramming,PushQueue,Stack,Structures,algorithms,downfirst-outfitness,geneticprogramming,
(0)
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2Evolving Data Structures Using Genetic Programming
W. Langdon. Research Note, RN/95/1. UCL, Gower Street, London, WC1E 6BT, UK, (января 1995)
17 лет назад , @brazovayeye
(ADF),
(FIFO)
Artificial
Automatic
Automatically
Data
Defined
Demes
Evolution,
First-in
Functions
Learning,
Machine
Object
Oriented
Pareto
Programming,
Push
Queue,
Stack,
Structures,
algorithms,
down
first-out
fitness,
genetic
programming,
(ADF),(FIFO)ArtificialAutomaticAutomaticallyDataDefinedDemesEvolution,First-inFunctionsLearning,MachineObjectOrientedParetoProgramming,PushQueue,Stack,Structures,algorithms,downfirst-outfitness,geneticprogramming,
(0)
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1Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming
F. Fernandez, M. Tomassini, и L. Vanneschi. Genetic Programming, Proceedings of EuroGP'2001, том 2038 из LNCS, стр. 51--63. Lake Como, Italy, Springer-Verlag, (18-20 April 2001)
17 лет назад , @brazovayeye
Distributed
Genetic
Multipopulation
Parallel
Parallelism,
Programming,
algorithms
algorithms,
evolutionary
genetic
programming,
structures,
DistributedGeneticMultipopulationParallelParallelism,Programming,algorithmsalgorithms,evolutionarygeneticprogramming,structures,
(0)
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1Discovery of context-specific ranking functions for effective information retrieval using genetic programming
W. Fan, M. Gordon, и P. Pathak. IEEE Transactions on Knowledge and Data Engineering, 16 (4): 523--527 (апреля 2004)
17 лет назад , @brazovayeye
Internet,
TREC
algorithms,
context-specific
contextual
corporate
data
data,
discovery,
engines,
fixed
function
genetic
information
intelligent
intranets,
mining
mining,
programming,
ranking
retrieval,
routing,
search
strategy,
structures,
term
text
tree
weighting
Internet,TRECalgorithms,context-specificcontextualcorporatedatadata,discovery,engines,fixedfunctiongeneticinformationintelligentintranets,miningmining,programming,rankingretrieval,routing,searchstrategy,structures,termtexttreeweighting
(0)
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1Evolutionary Development of Hierarchical Learning Structures
S. Elfwing, E. Uchibe, K. Doya, и H. Christensen. IEEE Transactions on Evolutionary Computation, 11 (2): 249--264 (апреля 2007)
17 лет назад , @brazovayeye
(artificial
Lamarckian
MAXQ
RL
algorithms,
decomposition
development,
evolutionary
foraging
genetic
hierarchical
intelligence),
learning
learning,
method,
programming,
reinforcement
structures,
task
task,
(artificialLamarckianMAXQRLalgorithms,decompositiondevelopment,evolutionaryforaginggenetichierarchicalintelligence),learninglearning,method,programming,reinforcementstructures,tasktask,
(0)
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2Characterizing the dynamics of symmetry breaking in genetic programming
J. Daida. GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, 1, стр. 799--806. Seattle, Washington, USA, ACM Press, (8-12 July 2006)
17 лет назад , @brazovayeye
algorithms,
analysis
breaking,
computational
data
design
genetic
geometry,
graphics
languages,
measurement,
methods,
patterns,
program
programming,
structures,
symmetry
synthesis,
techniques,
theory,
tree
algorithms,analysisbreaking,computationaldatadesigngeneticgeometry,graphicslanguages,measurement,methods,patterns,programprogramming,structures,symmetrysynthesis,techniques,theory,tree
(0)
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1What Makes a Problem GP-Hard? A Look at How Structure Affects Content
J. Daida. Genetic Programming Theory and Practice, глава 7, Kluwer, (2003)
17 лет назад , @brazovayeye
GP
GP-hard,
algorithms,
difficulty,
genetic
problem
problems
programming,
structures,
test
theory,
tree
GPGP-hard,algorithms,difficulty,geneticproblemproblemsprogramming,structures,testtheory,tree
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1GENETICA: A Computer Language That Supports General Formal Expression With Evolving Data Structures

1Giving Structural Descriptions of Tree-like Objects from Binary Images Using Genetic Programming

3Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001

2Regularization Approach to Inductive Genetic Programming

1Genetic Programming, Proceedings of EuroGP'2001

1Evolutionary synthesis of digital filter structures using genetic programming

1A Probabilistic Model of Size Drift

1ptGAs--Genetic Algorithms Evolving Noncoding Segments by Means of Promoter/Terminator Sequences

2Using Data Structures within Genetic Programming

2Using Data Structures within Genetic Programming

2Evolving Data Structures Using Genetic Programming

2Evolving Data Structures Using Genetic Programming

1Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming

1Discovery of context-specific ranking functions for effective information retrieval using genetic programming

1Evolutionary Development of Hierarchical Learning Structures

2Characterizing the dynamics of symmetry breaking in genetic programming

1What Makes a Problem GP-Hard? A Look at How Structure Affects Content

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