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Effective Structure Learning for Estimation of Distribution Algorithms via L1-Regularized Bayesian Networks : Apprendimento della struttura effettiva per la stima di algoritmi di distribuzione mediante network baynesiani regolarizzati-L1, in: International Journal of Advanced Robotic Systems |
Documenti accessibili
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Informazioni generali |
| Autore |
Xu, Hua; Yang, Jiadong; Jia, Peifa; Ding, Yi |
| Pubblicato |
InTech Open Access Publisher, 2013
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| Abstract |
Estimation of distribution algorithms (EDAs), as
an extension of genetic algorithms, samples new solutions
from the probabilistic model, which characterizes the
distribution of promising solutions in the search space at
each generation. This paper introduces and evaluates a
novel estimation of a distribution algorithm, called L1‐
regularized Bayesian optimization algorithm, L1BOA. In
L1BOA, Bayesian networks as probabilistic models are
learned in two steps. First, candidate parents of each
variable in Bayesian networks are detected by means of
L1‐regularized logistic regression, with the aim of leading
a sparse but nearly optimized network structure. Second,
the greedy search, which is restricted to the candidate
parent‐child pairs, is deployed to identify the final
structure. Compared with the Bayesian optimization
algorithm (BOA), L1BOA improves the efficiency of
structure learning due to the reduction and automated
control of network complexity introduced with L1‐
regularized learning. Experimental studies on different
types of benchmark problems show that L1BOA not only
outperforms BOA when no prior knowledge about
problem structure is available, but also achieves and even
exceeds the best performance of BOA that applies explicit
controls on network complexity. Furthermore, Bayesian
networks built by L1BOA and BOA during evolution are
analysed and compared, which demonstrates that L1BOA
is able to build simpler, yet more accurate probabilistic
models. |
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International Journal of Advanced Robotic Systems
Autore: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Pubblicato: 2004
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