id: 06108896
dt: a
an: 06108896
au: Ponce-De-Leon-Senti, Eunice Esther; Diaz-Diaz, Elva
ti: Adaptive evolutionary algorithm based on a cliqued Gibbs sampling over
    graphical Markov model structure.
so: Shakya, Siddhartha (ed.) et al., Markov networks in evolutionary
    computation. Berlin: Springer (ISBN 978-3-642-28899-9/hbk;
    978-3-642-28900-2/ebook). Adaptation, Learning, and Optimization 14,
    109-123 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-28900-2_7
ab: Summary: This chapter introduces estimation of distribution algorithms
    (EDAs) based on a learning strategy with two steps. The first step is
    based on the estimation of the searching sample complexity through an
    index based on sample entropy. The searching sample algorithm learns a
    tree and then uses a sample complexity index to prognose the missing
    edges to obtain the cliques of the structure of the estimating
    distribution adding more edges if necessary. In the second step a new
    population is generated by a new cliqued Gibbs sampler (CG-Sampler)
    that drags through the space of solutions driven by the cliques of the
    learned graphical Markov model. Two variants of this algorithm are
    compared, the adaptive tree cliqued EDA (ATC-EDA) and the adaptive
    extended tree cliqued EDA (AETC-EDA), and the Boltzmann selection
    procedure is used in CG-Sampler. They are tested with 5 known functions
    defined for 48, 50, 99 and 100 variables, and compared to the
    univariate marginal distribution algorithm (UMDA). The performance of
    the two algorithms compared to UMDA is equal for OneMax and ZeroMax
    functions. The ATC-EDA and AETC-EDA are better than the UMDA for the
    other 3 functions.
rv: