Master index | Index for Mateda2.0/ScriptsMateda/OptimizationScripts |
AffEDA_Deceptive3 | EXAMPLE 4: Aff_EDA with proportional selection and elitism 1 |
BayesianTree_IsingModel | EXAMPLE 10: |
DefaultEDA_NKRandom | EXAMPLE 9: Bayesian tree for a multiobjective function of the NK random |
DefaultEDA_OneMax | EXAMPLE 1: One Max Funtion. Default parameters |
DefaultEDA_TrapFunction | EXAMPLE 11: |
EBNA_Deceptive3 | EXAMPLE 2: Goldberg's deceptive function; BN K2 algorithm with BIC metric |
EBNA_MultiObj_SAT | % EXAMPLE 2: MULTIO-OBJECTIVE MAXSAT |
EBNA_PLS_MPC_NKRandom | EXAMPLE 16: |
GaussianMultivariate_OfflineHPProtein | EXAMPLE 12: Multivariate Gaussian EDA for the Offline HP Model continuous function |
GaussianNetwork_OfflineHPProtein | EXAMPLE 8: Gaussian network for the Offline HP Model continuous function |
GaussianUMDA_ContSumFunction | EXAMPLE 5: Gaussian UMDA for the continuous sum function in the interval [0,5] |
GaussianUMDA_OfflineHPProtein | EXAMPLE 7: Gaussian UMDA for the Offline HP Model continuous function |
MOA_Deceptive3 | EXAMPLE 3: MOA algorithm with exponential selection for Goldberg's deceptive function |
MixtureGaussianEDAs_OfflineHP | EXAMPLE 14: Mixtures of full Gaussian distributions |
MixtureGaussianEDAs_trajectory | EXAMPLE 15: Continuous EDAs that learn mixtures of distributions |
MkFDA_HPProtein | EXAMPLE 6: Markov Chain FDA for the HP protein model. |
TreeFDA_Deceptive3 | EXAMPLE 17: Goldberg's deceptive function; Tree EDA algorithm |
TreeFDA_HPProtein | EXAMPLE 18: Tree-FDA for the HP protein model (The tree structure is |
UMDA_OneMax | Creating UMDA for OneMax. |
VariantsGaussianEDAs_trajectory | EXAMPLE 13: Different variants of continuous EDAs for a spacecraft |