Friedrich > genetic

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1Emergent value orientation in self-organization of an animat
F. Krebs, and H. Bossel. Ecological Modelling, 96 (1-3): 143--164 (1997)
12 years ago by @fkrebs
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genetic
algorithms
animat
intelligence
artificial;life
artificial
systems
learning;value
orientors
future
studies
classifier
orientation
CESR
geneticalgorithmsanimatintelligenceartificial;lifeartificialsystemslearning;valueorientorsfuturestudiesclassifierorientationCESR
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1Comparative Application of Artificial Neural Networks and Genetic Algorithms for Multivariate Time Series Modelling of Algal Blooms in Freshwater Lakes
F. Recknagel, J. Bobbin, P. Whigham, and H. Wilson. HydroInformatic, 4 (2): 125--134 (2002)
17 years ago by @brazovayeye
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genetic
algorithms,
programming,
Ecology
geneticalgorithms,programming,Ecology
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2Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation
F. Recknagel, H. Cao, B. Kim, N. Takamura, and A. Welk. Ecological Informatics, 1 (2): 133--151 (April 2006)
17 years ago by @brazovayeye
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Cyanobacteria,
genetic
Non-supervised
Kasumigaura,
neural
modelling,
Soyang,
Recurrent
Lake
Time
Clustering,
Forecasting
Hybrid
artificial
algorithms,
supervised
Diatoms,
networks,
programming,
series
Ordination,
evolutionary
Cyanobacteria,geneticNon-supervisedKasumigaura,neuralmodelling,Soyang,RecurrentLakeTimeClustering,ForecastingHybridartificialalgorithms,supervisedDiatoms,networks,programming,seriesOrdination,evolutionary
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1An inductive approach to ecological time series modelling by evolutionary computation
P. Whigham, and F. Recknagel. Ecological Modelling, 146 (1-3): 275--287 (2001)
17 years ago by @brazovayeye
show all tags
genetic
algorithms,
programming
geneticalgorithms,programming
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1Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication
H. Cao, F. Recknagel, B. Kim, and N. Takamura. Ecological Informatics: Scope, Techniques and Applications, chapter 17, Springer-Verlag, Berlin, Heidelberg, New York, 2nd edition, (2006)
17 years ago by @brazovayeye
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genetic
algorithms,
programming
geneticalgorithms,programming
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1Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms
W. Chan, F. Recknagel, H. Cao, and H. Park. Water Research, 41 (10): 2247--2255 (May 2007)
17 years ago by @brazovayeye
show all tags
genetic
algorithms,
programming,
Microcystin,
Explanation
Forecasting,
Suwa,
Ordination,
Lake
Microcystis,
Clustering,
geneticalgorithms,programming,Microcystin,ExplanationForecasting,Suwa,Ordination,LakeMicrocystis,Clustering,
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1An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks
C. Friedrich, and C. Moraga. Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU '96), page 951--956. Granada, Spain, (1996)
17 years ago by @brazovayeye
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genetic
algorithms,
programming
geneticalgorithms,programming
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1Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms
D. Kim, H. Cao, K. Jeong, F. Recknagel, and G. Joo. Ecological Modelling, 203 (1-2): 147--156 (24 April 2007)Special Issue on Ecological Informatics: Biologically-Inspired Machine Learning, 4th Conference of the International Society for Ecological Informatics.
17 years ago by @brazovayeye
show all tags
genetic
aeruginosa,
model,
analysis
Microcystis
computation,
Machine
algorithms,
Sensitivity
learning,
Algebraic
Regulated
programming,
Evolutionary
river,
function
Rule-based
geneticaeruginosa,model,analysisMicrocystiscomputation,Machinealgorithms,Sensitivitylearning,AlgebraicRegulatedprogramming,Evolutionaryriver,functionRule-based
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2Discovery of Predictive Rule Sets for Chlorophyll-a Dynamics in the Nakdong River (Korea) by Means of the Hybrid Evolutionary Algorithm HEA
H. Cao, F. Recknagel, G. Joo, and D. Kim. Ecological Informatics, 1 (1): 43--53 (January 2006)
17 years ago by @brazovayeye
show all tags
genetic
Nakdong
Hybrid
Chl.a,
algorithms,
Sensitivity
programming,
analysis,
algorithm,
Rule
evolutionary
River
sets,
geneticNakdongHybridChl.a,algorithms,Sensitivityprogramming,analysis,algorithm,RuleevolutionaryRiversets,
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1Emergent value orientation in self-organization of an animat

1Comparative Application of Artificial Neural Networks and Genetic Algorithms for Multivariate Time Series Modelling of Algal Blooms in Freshwater Lakes

2Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation

1An inductive approach to ecological time series modelling by evolutionary computation

1Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication

1Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms

1An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks

1Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms

2Discovery of Predictive Rule Sets for Chlorophyll-a Dynamics in the Nakdong River (Korea) by Means of the Hybrid Evolutionary Algorithm HEA

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